Senior Software Developer at EasySLR · Gurugram · 3 - 6 years · ₹20L - ₹25L / yr · Bootstrapped · Posted 7 Apr 2026

EasySLR is pioneering the future of systematic literature reviews through AI and innovative technologies. Our platform, recognized by industry leaders and academic communities alike, redefines the way researchers conduct reviews, making the process faster, smarter, and more intuitive. We've been at the forefront of AI-driven research, presenting at major conferences and setting new standards in evidence synthesis. If you are a visionary leader with a passion for technology and a drive to make a significant impact, we want you to join our mission to transform the research landscape.
Responsibilities :
- Lead and mentor a team of talented engineers, fostering a culture of innovation, collaboration, and continuous learning.
- Architect and oversee the development of a scalable, high-performance platform that integrates cutting-edge AI technologies and industry best practices.
- Drive the engineering strategy, ensuring alignment with our product vision and business goals.
- Collaborate closely with cross-functional teams, including product, design, and AI experts, to deliver a world-class product experience.
- Ensure the robustness, security, and scalability of our infrastructure, leveraging your deep expertise in cloud computing and full-stack development.
- Stay ahead of emerging technologies, incorporating the latest advancements into our platform and maintaining our competitive edge.
- Cultivate a high-performing engineering team through effective hiring, coaching, and professional development opportunities.
Requirements :
- 4+ years of experience in software engineering, with a proven track record of leading high-performing engineering teams.
- Expertise in full-stack development, with hands-on experience in Python, Node.js, and frameworks like Next.js.
- Extensive experience with cloud platforms, particularly AWS, and familiarity with tools like AWS Lambda, AWS CDK, and containerization technologies.
- Strong background in designing and scaling complex, distributed systems with a focus on performance and security.
- Experience in AI/ML-driven product development is a significant plus.
- Exceptional problem-solving skills, with a strategic mindset and the ability to make data-driven decisions.
- Excellent communication skills, with the ability to articulate complex technical concepts to non-technical stakeholders.
What We Offer :
- The opportunity to lead a cutting-edge platform at the intersection of AI and systematic literature reviews.
- Competitive compensation and a clear path to executive leadership.
- A vibrant, inclusive work culture that values diversity, innovation, and work-life balance.
- The chance to make a meaningful impact in a fast-growing, AI-first SaaS company shaping the future of research.
Ready to lead the engineering efforts that will drive the next generation of AI-driven systematic reviews? Join us at EasySLR and be part of a team that's revolutionizing the research process. Apply now and embark on an exciting journey at the forefront of technology and innovation

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We are looking for an Engineering Lead to own the entire technology stack — from onboarding and underwriting to disbursals, repayments, and collections — and to build the engineering function into something genuinely AI-native.
What You'll Own
● Full tech stack: backend, frontend, infrastructure, integrations, and data pipelines
● Real-time underwriting and decisioning systems
● LOS/LMS architecture — onboarding, disbursals, repayments, and collections
● Integrations with bureaus, KYC providers, account aggregators, and payment gateways
● Reconciliation systems — disbursement, repayment, and NACH reconciliation end-to-end
● AWS infrastructure: scaling, reliability, uptime, and cloud cost ownership ● Data infrastructure for the credit and risk team — feature pipelines, model serving, experiment infrastructure
● Engineering leadership: hiring, sprint planning, code reviews, and execution standards
● Compliance systems: RBI guidelines, DPDP, KYC/AML, e-NACH, e-sign
AI-Native Engineering
This is a core part of the role, not a bonus. You will build a machine-readable knowledge base of the entire codebase — architecture, data models, service contracts, coding standards, decision history — so that AI agents working on code have the context to produce accurate, consistent output. You will build skills for code review, developer onboarding, and recurring engineering workflows. You will build a code review pipeline where agents do the first pass on every pull request. The knowledge base and the skills improve over time as the team grows and the product evolves.
What We're Looking For
● 7+ years in software engineering, with at least 2 years leading teams or architecture
● Strong hands-on experience with Python, Django, and React Native
● Deep expertise in AWS and cloud-native architecture
● Experience with both SQL and NoSQL databases
● Strong understanding of distributed systems, microservices, and API design
● Experience owning reconciliation or payment flow infrastructure in a lending or payments context
● Prior experience in fintech / NBFC / digital lending — mandatory
● Strong understanding of the full loan lifecycle — mandatory
● You have used LLMs seriously as engineering tools and have strong opinions about what makes AI-assisted development produce good output versus mediocre output
Bonus: Kubernetes / Kafka, AI/ML-driven underwriting, Account Aggregator framework, e-NACH / e-Sign / Video KYC integrations
What Success Looks Like
● scales with strong uptime, performance, and reliability
● Reconciliation runs cleanly — no financial discrepancies surface late ● A new engineer joins and is writing standard, correct code within their first week
● The credit team is never blocked on an engineering dependency
● Engineering health metrics are tracked and visibly improving
● AI agents are doing the structured first pass on code reviews, and the system gets smarter over time
About the role
You will be a pivotal member of the leadership team, responsible for driving the company's product and platform strategy while overseeing engineering excellence across AI, data, platform, DevOps and infrastructure. This is a mission-critical role for an engineering leader who is both strategic and hands-on, and who can build scalable technologies for global enterprise clients in a fast-paced innovation ecosystem.
Reports to: CEO · Location: Bengaluru, India — hybrid, 3 days a week in office
What you will do
Technology & product leadership
- Define and drive the technology vision, architecture and long-term platform roadmap.
- Oversee the architecture, design and delivery of highly scalable enterprise systems.
- Ensure engineering excellence, velocity and reliability across the product lifecycle.
Engineering & platform management
- Lead platform engineering, product technology, DevOps, infrastructure and Quality Engineering.
- Build robust cloud-native systems using the Azure, AWS and GCP ecosystems.
- Oversee operational effectiveness, including uptime, production reliability and cost optimisation.
Innovation & AI strategy
- Spearhead Mission AI by developing scalable, production-grade AI/ML and GenAI capabilities.
- Own the GenAI/LLM solutions architecture.
- Core specialisation in AI agents and autonomous workflows, data extraction and intelligent automation.
- Direct hands-on architectural oversight of large language models, applied AI and multi-layered deep learning products.
- Extensive expertise in building intelligent document automation systems — similar in complexity to patent parsing, legal workflow automation and structured decision intelligence tools.
Technical leadership
- A proven track record overseeing product architecture, tech strategy and cross-functional engineering execution across core full-stack and AI platforms.
- Collaborate with executive leadership on business strategy, client requirements and product delivery.
- Build, mentor and scale high-performing engineering teams with a growth mindset.
- Establish a strong technology culture grounded in ownership, innovation and continuous learning.
What success looks like
- Mission AI: build a world-class AI system leveraging GenAI, ML and enterprise-grade data engineering.
- Hyper-scaling: architect and scale the platform to match global industry leaders in the IP space.
- Culture building: develop a strong engineering organisation with high ownership, performance and innovation DNA.
Qualifications & experience
- Preferably under 15 years of enterprise software engineering experience across B2B SaaS or technology-first companies; fewer is fine for an exceptional engineer.
- A proven track record of taking early-stage AI/ML prototypes and scaling them into robust enterprise SaaS platforms featuring multi-agent orchestration, complex workflows and decision intelligence.
- Experienced in building and mentoring agile, lean startup teams of full-stack and machine learning engineers from the ground up.
- Proven leadership in defining and executing technology strategy and platform roadmaps.
- Extensive cloud-native engineering experience with Azure, AWS and GCP.
Technical expertise
- Strong full-stack engineering background (Java, Python, JavaScript frameworks).
- Expertise with JS frameworks such as React, Angular and Node.js.
- Experience building and scaling distributed systems and microservices.
- Strong knowledge of databases (SQL, NoSQL), data modelling and unstructured data management.
- Strong understanding of Agile methodologies and tools (Atlassian, Git, CI/CD pipelines).
Behavioural & leadership competencies
- Product and delivery management expertise, end to end, including delivery and customer support.
- Excellent communication, with the ability to influence executive stakeholders.
- High technical proficiency combined with strong business acumen.
- Strong analytical and decision-making skills.
Role & Responsibilities
Responsibilities
• Business: Immerse in operations until you think like an insider.
Rapidly acquire domain expertise through direct observation, translate between business and engineering seamlessly, and mentor engineers in your area on immersion. Influence senior stakeholders effectively, manage complex stakeholder landscapes with competing agendas, and build trust rapidly with new stakeholders.
• Delivery: Lead rapid delivery initiatives across teams in your area, coach on prototype-first approaches, and establish trust through consistent fast delivery. Build complete applications rapidly across any technology stack, select the right tools for each problem, and define clear criteria for prototype-to-production transitions.
• Generative AI: Architect RAG systems for complex use cases across teams, implement advanced techniques (hybrid search, reranking, query expansion), mentor engineers on RAG best practices, and establish RAG standards. Lead evaluation strategy across teams, establishing annotation guidelines, training human-calibrated LLM judges, and building evaluation pipelines that connect tracing to datasets to experiments.
• People: Build high-performing teams across your area, navigate complex interpersonal dynamics, foster psychological safety, and create environments where diverse perspectives are valued. Influence through communication at all levels — from frontline to executive. Handle difficult conversations skilfully and train engineers in your area on effective communication.
• AI-Augmented Development: Optimise AI tool usage across teams in your area, train engineers on AI-augmented and agentic engineering workflows, evaluate new AI development tools, and establish practices that balance AI speed with verification rigour.
• Scale: Design complex multi-component systems end-to-end, evaluate architectural options for large initiatives across teams, guide technical decisions for your area, and mentor engineers on architecture. Create debt reduction strategies across teams, influence roadmap decisions to include debt work, and teach engineers when to accept debt for speed versus when to invest in quality.
• Documentation: Define documentation standards across teams in your area, create documentation systems and templates, train engineers on spec-driven development, and ensure documentation quality across projects. Lead pattern generalization initiatives, defining criteria for when to generalize versus keep custom.
• Reliability: Define reliability standards across teams in your area, drive post-incident improvements systematically, design capacity planning processes, andmentor engineers on SRE practices.
Ideal Candidate
- Strong Staff Software Engineer / FDE profile (full-stack + production GenAI, multi-team technical leadership)
- Mandatory (Experience 1) – Must have 7+ years of relevant professional software engineering experience, with demonstrated full-stack delivery across backend and frontend.
- Mandatory (Experience 2) – Must have deep production experience with Python AND JavaScript/TypeScript, working comfortably across the full stack.
- Mandatory (Experience 3) – Must have 2+ years of experience in generative AI applications developement — LLM integrations, vector databases, RAG systems, and evaluation pipelines
- Mandatory (Experience 4) – Must have strong experience with modern frontend frameworks (Next.js / React) and backend API development.
- Mandatory (Experience 5) – Must have extensive experience with cloud platforms (AWS preferred; Azure/GCP valued), including infrastructure-as-code (CloudFormation / Terraform).
- Mandatory (Experience 6) – Must have working knowledge of multiple database paradigms — relational (PostgreSQL), document, and key-value (Redis) — with ability to select the right storage per problem.
- Mandatory (Experience 7) – Must have strong experience with CI/CD pipelines (e.g. GitHub Actions), containerization, and production deployment strategies.
- Mandatory (Experience 8) – Must have demonstrable fluency with AI coding tools (Claude Code, Cursor, GitHub Copilot, or similar) and proven ability to design agentic engineering workflows and train teams on them
- Preferred (Experience) – Advanced RAG techniques — hybrid search, reranking, query expansion — and establishing RAG standards across teams
Job Description:
Head of Engineering — Chennai (Onsite)About the Role
Job Details
Position: Head of Engineering
Department: Engineering
Experience: 6–8 Years
Employment Type: Full-time
Location: Chennai, On-site
Salary: Up to ₹24,00,000 per annum
About the Role
We’re looking for a Head of Engineering to take end-to-end ownership of engineering for a fast-moving product. You’ll be responsible for architecture, execution, product quality, engineering culture, and team growth.
This is a hands-on leadership role. You’ll spend approximately 60% of your time coding and reviewing code and 40% on product, people, and engineering leadership.
What You’ll Do
- Own engineering end to end, including architecture, development, delivery, quality, and hiring.
- Stay hands-on with the codebase, spending roughly 60% of your time writing and reviewing code.
- Establish and continuously improve the engineering workflow: how the team plans, builds, reviews, ships, and measures.
- Improve development velocity and reduce cycle time without compromising product quality.
- Drive AI-assisted software development across the engineering team using Claude Code and similar agentic coding tools as a core productivity lever.
- Establish practical systems and workflows that demonstrate measurable improvements in engineering throughput.
- Work closely with the founders on product strategy, technical trade-offs, priorities, and what to build next.
- Build, mentor, and scale a high-performing engineering team.
What We’re Looking For
- 6–8 years of professional software engineering experience.
- Experience taking at least one product from zero to launch.
- Experience building and scaling at least one product to a large user base.
- Strong hands-on expertise in Python and Node.js.
- Comfortable working with React Native / Expo.
- Strong understanding of PostgreSQL and database fundamentals.
- Previous experience leading a small engineering team.
- A leadership style based on ownership and technical example, rather than simply assigning tickets.
- Daily experience with agentic coding tools, with the ability to demonstrate how they have improved development speed and team throughput.
- Strong product instincts and the ability to form opinions about user problems, not just technical implementation.
- Based in Chennai or willing to relocate.
How We Work
We operate with high pace, high ownership, and short feedback loops.
You’ll be expected to get close to the product, understand the users, make decisions quickly, and ship within your first week.
If you enjoy building products from the ground up, staying close to the code, and using AI to fundamentally improve how engineering teams work, we’d love to hear from you.
How to Apply
Join Aura Gold and be part of building a high-performing team from the ground up!
Ability to commute/relocate:
- Chennai, Tamil Nadu (Chennai, Chennai District): Reliably commute or planning to relocate before starting work (Preferred)
Education:
- Bachelor's (Preferred)
Language:
- English, Tamil , Hindi (Required)
Location:
- Chennai, Tamil Nadu (Chennai, Chennai District) (Required)
Work Location: In person
About the Role
We are seeking a hands-on Tech Lead to design, build, and integrate AI-driven systems that automate and enhance real-world business workflows. This is a high-impact role for someone who enjoys full-stack ownership — from backend AI architecture to frontend user experiences — and can align engineering decisions with measurable product outcomes.
You will begin as a strong individual contributor, independently architecting and deploying AI-powered solutions. As the product portfolio scales, you will lead a distributed team across India and Australia, acting as a System Integrator to align engineering, data, and AI contributions into cohesive production systems.
Example Project
Design and deploy a multi-agent AI system to automate critical stages of a company’s sales cycle, including:
- Generating client proposals using historical SharePoint data and CRM insights
- Summarizing meeting transcripts
- Drafting follow-up communications
- Feeding structured insights into dashboards and workflow tools
The solution will combine RAG pipelines, LLM reasoning, and React-based interfaces to deliver measurable productivity gains.
Key Responsibilities
- Architect and implement AI workflows using LLMs, vector databases, and automation frameworks
- Act as a System Integrator, coordinating deliverables across distributed engineering and AI teams
- Develop frontend interfaces using React/JavaScript to enable seamless human-AI collaboration
- Design APIs and microservices integrating AI systems with enterprise platforms (SharePoint, Teams, Databricks, Azure)
- Drive architecture decisions balancing scalability, performance, and security
- Collaborate with product managers, clients, and data teams to translate business use cases into production-ready systems
- Mentor junior engineers and evolve into a broader leadership role as the team grows
Ideal Candidate Profile
Experience Requirements
- 5+ years in full-stack development (Python backend + React/JavaScript frontend)
- Strong experience in API and microservice integration
- 2+ years leading technical teams and coordinating distributed engineering efforts
- 1+ year of hands-on AI project experience (LLMs, Transformers, LangChain, OpenAI/Azure AI frameworks)
- Prior experience in B2B SaaS environments, particularly in AI, automation, or enterprise productivity solutions
Technical Expertise
- Designing and implementing AI workflows including RAG pipelines, vector databases, and prompt orchestration
- Ensuring backend and AI systems are scalable, reliable, observable, and secure
- Familiarity with enterprise integrations (SharePoint, Teams, Databricks, Azure)
- Experience building production-grade AI systems within enterprise SaaS ecosystems
Cognizant is one of the world's leading professional services companies, transforming clients' business, operating, and technology models for the digital era. Our unique industry-based, consultative approach helps clients envision, build, and run more innovative and efficient businesses. Headquartered in the U.S., Cognizant (a member of the NASDAQ-100 and one of Forbes World's Best Employers 2025) is consistently listed among the most admired companies in the world.
This role is part of Cognizant IOA (Intuitive Operations and Automation) — and more specifically, a product team with its own origin story. We started as Matterway, a German startup building automation software from scratch. We were acquired by Cognizant, but we kept our team, our culture, our pace, and our way of working. We move like a startup, think like a startup, and now do it with the backing of a global company.
We combine human expertise with intelligent automation, ML operations, and digital experience solutions to help technology and digital-native companies build smarter, more adaptive operating models.
What's the opportunity?
We are looking for Senior Product Engineers to join our team. We're a tight crew of 5 senior engineers, from different corners of the world. We're hiring 3 more people, and those people will shape what this team becomes.
We're building a platform to support the agentification of our operations. You'll work across a suite of products including a cloud platform for process optimization, desktop application, browser extension, and specialized AI agents.
This is genuinely frontier work: we don't follow AI trends, we implement them. New model drops on a Monday; we've prototyped with it by Wednesday.
We believe this is the most exciting time to join. Be part of the founding R&D talent in this area and help set the technical bar for everything that comes next.
What will you be doing?
Your day-to-day responsibilities:
- Design and evolve the core architecture of our platform
- Set technical standards and take ownership of complex projects end-to-end
- Work on a well-architected, fast-evolving TypeScript codebase using technologies such as React, Node.js, Electron, and GraphQL
- Collaborate with product managers, designers, and engineers to solve challenging technical and business problems, with real autonomy
- Participate in the full development lifecycle: scoping, design, estimation, coding, testing, debugging, code reviews, maintenance, and support
- Evaluate and implement proofs of concept using the latest AI tools and models
You will know you're successful if:
- You develop projects independently until they deliver their intended business value
- You keep top-notch software quality standards about design, implementation, and documentation
- You're the person who brings something new to the team's radar, a model, a technique, a tool, before anyone else has heard of it
What makes you a great fit
Must-haves:
- Excellent English (C1+) with exceptional written and verbal communication
- Strong proficiency with TypeScript across frontend and backend, including building and maintaining Node.js-based services
- Experience in frontend development using modern frameworks such as Vue.js or React
- Knowledge of any E2E testing framework such as Playwright, Puppeteer, Selenium, Cypress
- Ability to work independently and take ownership of deliverables
- Strong technical fundamentals, genuine intellectual curiosity, and a drive to keep getting better
- You follow AI closely, not just as a user but as someone who reads the papers, watches the releases, and has opinions. You're excited about where this is going and want to be building at the edge of it
Nice-to-haves:
- Familiarity with AI-coding tooling (Claude Code, Cursor, OpenCode, etc.)
- Experience with cloud infrastructure (AWS, Azure or GCP) and infra-as-code deployment
- Having built or maintained CI/CD pipelines or workflows, especially GitHub actions
Why join us?
- Be at the cutting edge of AI agents and applying LLMs to real-world use cases, not in theory but in production
- Work on hard problems with high agency: your ideas get heard; your decisions get shipped
- Join a small but growing internationally distributed team.
- giving you real influence over how it evolves
- The resources of a global company with the speed and culture of a startup
- Remote-first set-up, with the option to work from one of our 13 locations in India: Chennai, Bangalore, Hyderabad, Pune, Mumbai, Gurgaon, Noida, Kolkata, Kochi, Coimbatore, Bhubaneswar, Mangalore, and Indore
- Flexible working hours
- Collaborative, pragmatic engineering culture focused on outcomes
- Fast-growing team within Cognizant, with access to global resources and real career opportunities
Roles & Responsibilities
- Write, review and merge production code across Vue.js frontend and Node.js backend
- Own code quality for the India team — set review standards and refactor technical debt
- Debug across the full stack — from Vue rendering issues to Mongo aggregations to Kubernetes pod issues
- Guide the team on day-to-day development decisions and step in on unsolved problems
- Design solutions for new modules from spec to schema to API contract to implementation
- Own MongoDB data modelling decisions — document structure, embedding vs referencing, index strategy
- Review Node.js service design: API structure, error handling, validation, authentication, rate limiting, background processing
- Make and document build-vs-buy calls on third-party services with cost and integration effort
- Ensure the platform stays stable and performant on a live, revenue-generating system
- Demonstrate daily working use of AI coding tools and spec-driven development practice
- Judge AI output quality and know when tasks should not be handed to agents
- Set up and maintain repo-level context files and make the team's AI setup consistent
- Coach the team on AI tools while holding the line on quality
- Work with LLM APIs, embeddings, semantic search and understand where AI features help
- Run sprint planning, estimation, standups and retros for the India team
- Break requirements into technical tasks with clear acceptance criteria
- Track velocity and flag slippage and blockers early
- Own release coordination — deployment planning, rollback strategy and post-release monitoring
- Line-manage 6–10 engineers across web, mobile and QA — 1:1s, feedback, performance conversations and growth planning
- Mentor mid-level engineers into senior ones and build depth
- Participate in hiring — technical screening, interviewing and onboarding
- Keep the team's engineering practices consistent: documentation, testing, branching, incident handling
- Act as the main technical point of contact between the India team and the UAE office product and business stakeholders
- Communicate clearly to mixed technical and non-technical audiences — status, risk, trade-offs and time cost
- Manage the time-zone gap with structured overlap hours, written updates and async documentation
- Own technical delivery on modules including product catalogue, pricing, ordering, workflows, order management, payments, invoicing and vendor onboarding
Ideal Candidate
1Strong Hands-On Technical Team Lead Profile (Vue.js + Node.js) with AI-assisted development and B2B e-commerce exposure2Mandatory (Experience) - Must have 8+ years of software engineering experience with at least 2+ years leading a team of 5 or more engineers3Mandatory (Tech skill 1): Must have strong, current, hands-on Vue.js experience including component architecture, state management and performance optimisation4Mandatory (Tech skill 2): Must have strong, current, hands-on Node.js and Express experience for production API development at scale5Mandatory (Tech skill 3): Must have deep MongoDB experience including aggregation pipelines, index design, query performance tuning and schema decisions6Mandatory (Tech skill 4): Must have AWS experience particularly Kubernetes/EKS with deployments, resource limits, health probes and production monitoring7Mandatory (Tech skill 5): Must have working understanding of Flutter including widget architecture, state management, platform channels and Play Store/App Store release pipelines8Mandatory (Tech skill 6): Must have demonstrable, daily professional use of AI coding tools (Claude Code, Cursor, GitHub Copilot, Gemini CLI or equivalent) with real examples9Mandatory (Tech skill 7): Must have experience with spec-driven development including writing structured requirement and design documents and reviewing AI-generated output against acceptance criteria10Mandatory (Tech skill 8): Must have experience setting up and maintaining repo-level context files, rules files and custom commands for AI development11Mandatory (Tech skill 9): Must have Git and CI/CD experience including branching strategy, review workflow and automated pipelines12Mandatory (Leadership): Must have line-managed engineers (1:1s, feedback, performance, growth), mentored mid-to-senior, and participated in hiring13Mandatory (Communication): Must communicate clearly to mixed technical/non-technical audiences and manage a time-zone gap with structured overlap and async documentation (works with the UAE office)14Mandatory (Education): Bachelor's or Master's in Computer Science, Engineering or related field15Mandatory (Domain): Background in B2B e-commerce, wholesale/distribution or marketplace platforms16Preferred (Offshore Experience): Experience working as the offshore technical counterpart to an overseas leadership team17Preferred (AI Integration): Experience integrating LLM APIs into a production product18Preferred (Observability): Exposure to observability tooling such as CloudWatch, Datadog or Sentry and analytics instrumentation19Preferred (Third-party Integrations): Experience with payment gateways, e-signature, logistics APIs, mapping and geolocation integrations
Read This Before Anything Else
We have 6 developers who can ship. What we don't have is someone who turns that into a real engineering function: real architecture, real leverage, real AI-driven advantage. If that gap sounds like an opportunity rather than a headache, you're in the right place. If it sounds like a lot of undefined work with no playbook handed to you, this one probably isn't for you. That's completely okay. There are plenty of great roles that fit differently.
About CraftMyPlate
CraftMyPlate is Hyderabad's go-to platform for food experiences for micro-events: house parties, birthdays, office celebrations, festive gatherings, and more. We're building the operating system for how India discovers, customises, and orders food for smaller events. We're backed by established founders and investors, and we're funded and growing fast. The next phase of that growth runs through engineering.
Where We Stand
Some numbers, because they matter more than adjectives. Order volume has grown 50x in two years, and we're compounding at roughly 3x year over year, without giving up equity to fund it. That means the business runs on its own economics. The growth is real demand, not runway bought with dilution, and every efficient architectural decision this role makes directly protects that.
Most people size up an opportunity by asking what's going to change in ten years. The more useful question, and the one this company is built around, is what won't change. People will keep gathering. They'll keep celebrating, hosting, and marking festivals, in 10 years and in 20. That permanence is the bet. You're not building infrastructure for a trend cycle. You're building for a category that outlasts the current AI wave, the next funding round, and probably us too.
The Technical Reality
Here's an honest read of the engineering problem, not a sanitized version of it.
Event-driven commerce doesn't scale like typical e-commerce. Demand isn't smooth, it's spiky: weekends, festival calendars, and event dates create real load concentration, and each order is tied to a hard deadline that can't slip the way a shipped package can. That has direct architectural consequences: systems need to handle bursty, unpredictable traffic without paying for idle capacity the rest of the time, which is exactly why we're serverless-first on AWS rather than running a fixed fleet sized for peak.
Underneath that, every order touches multiple systems that have to stay consistent: kitchen and vendor fulfillment status, inventory across partners, payment gateway settlement, and refunds, often in real time and often across more than one vendor for a single event. Getting that consistency right across SQL and NoSQL stores, without it becoming a source of support tickets and manual reconciliation, is a real architecture problem, not a CRUD problem.
The AI-agent layer is the next lever, and it's a business lever as much as a technical one. Every workflow we can hand to a well-orchestrated agent instead of a new hire is a workflow that scales without adding headcount, which is exactly how a company grows 3x a year without diluting equity to fund the team behind it. That's why agent orchestration across multiple LLMs, using LangGraph, sits in the "go deep" tier of this role rather than being a nice-to-have.
You'll likely find some of this framing right and some of it worth challenging once you're actually in the codebase. That's expected, and honestly preferred over someone who just nods along.
Why This Role Exists
You'll be the most senior technical person in the company, reporting directly to the founder. Not a manager brought in to run standups. An owner. You set the architecture, you write code yourself, and you make the team materially better. You also own where AI and automation take this company next, starting with our first in-house AI agent product (details shared in the interview), and expanding from there into how the company runs, department by department: HR, finance, marketing, design, development, all sitting on an engineering layer that you design.
If you've outgrown a role where you plan but don't build, or where good ideas die in a committee, this is built to be the opposite of that.
What You'll Own
- Architecture, end to end. Scalable, cost-efficient systems from day one, not "fix it later" engineering. You own the decisions and their long-term consequences.
- Hands-on building. You are still writing code and shipping. This isn't a seat where you review other people's work all day. You lead by building.
- The engineering team. Directly manage, mentor, and level up our 6 developers. Build the technical bar, the review culture, and the calibration that lets the team ship independently.
- The AI-agent roadmap. Own the architecture behind our first AI agent product, then the broader strategy for AI agents and automation across every function in the company, with engineering as the layer underneath all of it.
- Team scaling. Build the next layer of leads under you so execution quality scales without you being the bottleneck.
- Technical accountability. When something breaks, you fix it. You don't escalate and wait.
Our Stack, and the Depth We Expect
Not everything on this list needs the same level of mastery. Some of it you need to own at an architectural level. The rest you need to be strong enough to build yourself, direct the team on, or delegate to AI agents with confidence.
Go deep here. This is where the real architecture decisions live, and where the business impact is highest:
- AWS, serverless first. You should be genuinely well versed in AWS application development, not just "have used AWS." You should be able to design and guide serverless architecture (Lambda, API Gateway, DynamoDB, Step Functions, and similar) as our default way of building, because our demand curve is spiky by nature and fixed infrastructure is money left on the table.
- TypeScript, our primary language across backend and frontend.
- Agent orchestration across multiple LLMs, using LangGraph. This is core to our AI roadmap and our path to scaling operations without scaling headcount. You own how it's architected, not just how it's used.
Working proficiency. Build it yourself, direct the team, or hand it to an AI agent and know if the output is right.
This Is You If
- You've built and shipped real production systems yourself, not just reviewed other people's architecture from a distance.
- You go deep wherever the problem is, and you're comfortable owning the exact stack described above, not just "full-stack" in the abstract.
- You've made engineers around you measurably better, whether or not you've held the title for it yet.
- You're already using AI coding tools and agents seriously, like Claude, Cursor, or similar tools, as part of how you build, not as something you tried once. We'll likely explore this together in the interview.
- You have a bias toward leverage over hours. You'd rather automate or systematize a problem than grind through it. But when something's live and needs to be done right, you see it through completely, with no half-finished work.
- You want to build something for years, not land somewhere comfortable. We'll know the difference from how you talk about your last three years.
This Might Not Be the Right Fit If
- You'd prefer a stable, well-defined role with clear boundaries and someone else making the calls. That's a fair thing to want, just not what this is.
- You'd rather receive direction than bring us architecture and AI strategy yourself.
- You haven't yet gotten hands-on with AI coding tools in your daily work.
- You're drawn more to the title than the work behind it.
If none of that sounds like you, we'd love to hear from you.
Requirements
- 5 to 7 years of experience in software engineering, with real ownership of architecture-level decisions, not just feature delivery.
- Prior experience leading or mentoring engineers, formally or informally.
- Tier-1 or Tier-1+ engineering college strongly preferred (IIT, BITS, top NIT tier, or equivalent). We'll consider other institutions only with clearly commendable, verifiable work: real systems you can walk us through in depth, strong open-source contributions, or a track record that speaks for itself. Pedigree is a proxy for speed, not a checkbox. We test for the underlying ability regardless.
- Comfortable in an early-stage environment: undefined problems, few processes, and the expectation that you help define both.
Compensation
Competitive, with equity. We're formalizing a structured ESOP program alongside this hire. Specific numbers are discussed directly in later interview rounds.
If reading this got you a little excited about what you'd build here, we'd genuinely love to talk. If it didn't quite land, no hard feelings. We just want the right fit for both sides.
About Us
We believe the future of software development is AI-native — where engineers operate at a higher level of abstraction and quality remains non-negotiable.
Incubyte is a software craft consultancy where the “how” of building software matters as much as the “what”.
We partner with companies of all sizes, from helping enterprises build, scale, and modernize to early-stage founders bring their ideas to life.
Our engineers operate in an AI-native development model, using AI as a collaborator across the SDLC to accelerate development while upholding the discipline of software craftsmanship. Guided by Software Craftsmanship and Extreme Programming practices, we build reliable, maintainable, and scalable systems with speed, without compromising quality. If this way of building software resonates with you, we’d like to talk.
Our Guiding Principles
These principles define how we work at Incubyte. They are non-negotiable.
Relentless Pursuit of Quality with Pragmatism
We build high-quality systems without losing sight of delivery.
Extreme Ownership
We take responsibility end-to-end for decisions, execution, and outcomes.
Proactive Collaboration
We collaborate closely, challenge each other, and solve problems together.
Active Pursuit of Mastery
We continuously improve our craft and raise our bar.
Invite, Give, and Act on Feedback
We seek, give, and act on feedback to get better every day.
Ensuring Client Success
We act as trusted partners and focus on real outcomes, not just output.
Experience Level
This role is ideal for engineers with total 3+ years of experience with a proven track record of shipping complex projects successfully.
An experienced individual contributor and leader who thrives in large, complex projects with widespread impact.
What You’ll Do as a Software Craftsperson
- Design and build high-quality, maintainable systems using disciplined engineering practices such as TDD, continuous refactoring, and pair programming
- Operate in an AI-native development model, using AI as a collaborator to explore architecture and design, accelerate development, and continuously improve systems while applying strong judgment to ensure that speed never compromises quality
- Take end-to-end ownership of outcomes from problem understanding and system design to implementation, deployment, and operation in production
- Make thoughtful design decisions that balance simplicity, scalability, and long-term maintainability in real-world systems
- Maintain a high bar for engineering quality through rigorous testing, code reviews, and continuous feedback
- Investigate and resolve production issues, and implement systemic improvements to prevent recurrence
- Work directly with clients, navigate ambiguity, and translate business problems into well-designed technical solutions
- Contribute to improving team practices, tooling, and systems to raise the overall quality and effectiveness of engineering
Requirements
What You’ll Bring
- 3+ years of experience building high-quality, production systems (flexible based on demonstrated capability)
- Strong fundamentals in software engineering, including object-oriented design, system design, and testing practices such as TDD
- Demonstrated ability to build simple, maintainable, and scalable systems with a focus on long-term reliability
- Proficiency in one or more modern technologies, Python, PHP, JavaScript, or TypeScript, with the ability to learn new technologies quickly
- Deep experience working with Git in collaborative environments, including managing shared codebases, conducting code reviews, and maintaining a high bar for quality
- Ability to operate effectively in an AI-native workflow using AI as a collaborator to explore solutions and accelerate development, while applying strong judgment to ensure correctness, quality, and maintainability
- Clear thinking and strong problem-solving ability, with the capacity to break down complex problems into simple, well-structured solutions
- A strong sense of ownership — you take responsibility for outcomes, care deeply about quality, and are not comfortable shipping work that does not meet your standards.
Benefits
Life at Incubyte
We are a remote-first company with structured flexibility. Teams commit to shared rhythms during core hours, ensuring smooth collaboration while maintaining autonomy. Twice a year, we come together in person for a co-working sprint and once a year for a retreat - with all travel expenses covered.
Our environment is built for crafters: pairing, refactoring, experimenting with AI, and pushing the boundaries of software excellence. We are all lifelong learners, and our work is our passion.
Perks
- Dedicated learning & development budget.
- Sponsorship for conference talks.
- Comprehensive medical & term insurance.
- Employee-friendly leave policies.
- Home Office fund
- Medical Insurance
Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 5 years of experience with software development in one or more programming languages.
- 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.
- 3 years of experience with one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.
- 3 years of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).
Preferred qualifications:
- Master’s degree or PhD in Computer Science or a related field with a focus on Artificial Intelligence (AI) or Machine Learning (ML).
- 7 years of software experience designing and deploying large-scale AI/ML applications and complex models.
- Experience with MLOps, establishing best practices for production model monitoring, maintenance, and lifecycle management.
- Experience leading and mentoring engineers, and translating complex business objectives into scalable AI-driven roadmaps.
- Experience architecting scalable data pipelines using BigQuery, Beam, or Dataflow, and deploying models on Google Cloud Platform.
- Understanding of Python and frameworks like TensorFlow, JAX, and Scikit-learn, with deep statistical modeling knowledge.
About the job
The People Operations Engineering team is dedicated to building innovative and scalable technology solutions that empower a global workforce. Leveraging the power of data, AI, and machine learning, the team optimizes HR processes, enhances employee experiences, and drives organizational efficiency. Joining this team offers the opportunity to lead the application of AI to solve complex, real-world business challenges.
We are seeking a highly experienced and results-oriented Senior AI Software Engineer to join the Human Resources Engineering (HRE) team in Hyderabad.
In this role, you will serve as a technical leader, driving the design, development, and deployment of sophisticated AI-powered solutions that significantly impact employees across the organization. You will work on large-scale infrastructure, mentor a talented team of engineers, collaborate with cross-functional stakeholders, and help shape the future of HR through innovative AI applications. You will be responsible for translating complex business challenges and strategic objectives into scalable, efficient AI/ML systems.
As part of the Corporate Engineering organization, you will build world-class business solutions that support a global enterprise. The team delivers end-to-end platforms, tools, and experiences that enable employees to work more effectively and drive innovation across the organization.
Responsibilities
- Lead the technical design and architecture of complex, scalable AI/ML systems and infrastructure within the HR engineering domain, ensuring high reliability, performance, and long-term sustainability.
- Drive the development of advanced AI/ML algorithms for HR applications such as talent acquisition and employee engagement using Python and frameworks like TensorFlow and JAX.
- Architect large-scale data pipelines for data ingestion, cleaning, and feature engineering using modern data processing frameworks (e.g., Beam, Dataflow), while leading root cause analysis and system troubleshooting.
- Own the end-to-end MLOps lifecycle, including model deployment, monitoring, optimization, and maintenance, while establishing best practices for software development and code quality.
- Collaborate with cross-functional stakeholders to translate business needs into scalable AI/ML solutions, mentor engineers, and drive the adoption of emerging AI technologies across the organization.







