Machine Learning Lead

About the Company

As the world moves toward AI-native applications, one element matters more than anything: data.

We are building a platform that helps organizations unlock the value hidden within customer feedback. By centralizing data from multiple sources—including CRM platforms, support systems, collaboration tools, surveys, community forums, and customer conversations—into a unified intelligence layer, we enable teams to understand customer needs and make better product decisions.

Built on top of this foundation is an AI-powered platform that allows teams to query, monitor, and act on customer intelligence directly within their workflows. Our customers include some of the fastest-growing product-led technology companies as well as large global enterprises.

Backed by leading global venture capital firms, our engineering culture is AI-native from the ground up. Agents, LLMs, and applied machine learning are not side projects—they are central to how we build products. We operate at the cutting edge of agentic systems and continuously push the boundaries of what modern AI applications can achieve.

What You Will Do

• Convert product direction into phased technical plans, including estimates, milestones, dependencies, and risk assessments.
• Make thoughtful trade-offs across correctness, latency, user experience, AI/model behavior, reliability, cost, and delivery speed.
• Define clear ownership boundaries across frontend applications, BFF layers, backend services, agent workflows, ML platforms, and data contracts.
• Review ERDs, PRDs, design documents, and implementation plans to identify gaps before they become execution challenges.
• Drive planning and execution for complex initiatives while empowering area leads and senior engineers to maintain ownership.
• Partner closely with Product, Design, Engineering, and GTM teams to turn ambiguous problem statements into executable solutions.
• Influence technical direction through strong engineering judgment, architectural leadership, and hands-on involvement.
• Mentor senior engineers and contribute to engineering excellence through reviews, incident analysis, and proactive risk management.

What It Takes

• 9+ years of experience as a Senior Engineer, Technical Lead, or equivalent role.
• Demonstrated experience owning architecture and execution for large-scale product or platform initiatives spanning multiple teams.
• Strong foundation in distributed systems and modern software architecture.
• Exposure to agentic AI systems, machine learning engineering, MLOps platforms, and frontend/BFF architectures.
• Ability to own end-to-end product and technical problems rather than specializing exclusively in a single layer of the stack.
• Proven track record of converting ambiguity into actionable engineering plans.
• Excellent written communication skills, including design documentation, architectural reviews, trade-off analyses, and RCA documentation.
• Ability to challenge requirements constructively and collaborate effectively across Product, Design, Engineering, and GTM functions.
• High ownership mindset, attention to detail, and a low-ego, collaborative approach.

We’re Hiring | Technology Lead – Java & AWS

Locations: Kochi | Trivandrum | Bangalore

Experience: 15–18 Years

Key Skills: Java, AWS, AI/ML

What We’re Looking For

Strong hands-on expertise in Java and enterprise application development.

Solid experience with AWS and cloud-based architectures.

Practical experience implementing AI/ML solutions.

Expertise in solution architecture, system integration, and scalable application design.

Proven ability to lead technical initiatives while remaining actively involved in coding, design, and troubleshooting.

Experience delivering end-to-end enterprise solutions.

Staff Engineer - AI:

Location : India, Remote

Job Description

Egnyte is seeking an experienced Staff Software Engineer to join our Engineering department. The Engineering department builds large distributed components and services that run Egnyte's Cloud Platform. Our code serves billions of requests per day with sub-second latency in a fault-tolerant environment. We process and analyze millions of files and events daily. Some of the responsibilities for this department include Egnyte's Cloud File System, Content Classification, Content Lifecycle Management, User Behavior Analysis, Object Store, Metadata Stores, Search Systems, Recommendations Systems, Synchronization, and intelligent caching of multi-petabyte datasets. We are looking for candidates with a shared passion for building large-scale distributed systems and a keen sense for tackling complexities that come with scaling through multiple orders of magnitude.

In this role, you will (But are not limited to):

• Design and develop highly scalable and resilient cloud architecture that seamlessly integrates with on-premises systems
• Drive the team’s goals and technical direction to find and pursue technical opportunities that make Egnyte’s cloud platform more efficient
• Effectively communicate complex design and architecture details
• Understand company and industry-wide trends to help develop new technologies
• Conceptualize, develop, and implement changes that prevent key systems from becoming unreliable, under-utilized, or unsupported
• Own all aspects of critical software projects from design to implementation, QA, deployment, and monitoring

Qualifications

• BS, MS, or PhD. in Computer Science or related technical field, or equivalent practical experience
• 8-15 years of professional experience in engineering with a history of technical innovation
• Experience providing technical leadership to engineers

Bonus Qualifications (Good to Have)

• The breadth of knowledge across infrastructure domains, with the ability to reason about everything from data center machine software to database solutions to machine learning infrastructure to front-end web or mobile applications
• Demonstrated success in designing and developing large-scale, complex systems
• Expertise with multi-tenant, highly complex, cloud solutions; experience with Hybrid and/or on-premises solutions desired

About Egnyte

In a content critical age, Egnyte fuels business growth by enabling content-rich business processes, while also providing organizations with visibility and control over their content assets. Egnyte’s cloud-native content services platform leverages the industry’s leading content intelligence engine to deliver a simple, secure, and vendor-neutral foundation for managing enterprise content across business applications and storage repositories. More than 16,000 customers trust Egnyte to enhance employee productivity, automate data management, and reduce file-sharing cost and complexity. Investors include Google Ventures, Kleiner Perkins, Caufield & Byers, and Goldman Sachs. For more information, visit www.egnyte.com

Senior Technical Manager

Primary objective: Own engineering teams, architecture, delivery governance and successful execution of product and AI initiatives across DocTutorials.

Location

Hyderabad / Chennai

Employment

Full-time

Experience

10+ years

Role Summary

The Senior Technical Manager combines technical leadership, people management, product delivery and project execution. The role is accountable for team outcomes, engineering quality, timelines, production stability and the disciplined delivery of AI projects.

Key Responsibilities

1. Technology and Architecture Leadership

·       Provide direction for Python FastAPI, React/Next.js, data, cloud and AI architectures.

·       Review solution designs for security, scalability, maintainability, performance and cost.

·       Define engineering standards and resolve complex cross-team technical risks.

·       Remain technically hands-on through architecture reviews, critical code reviews and incident debugging.

2. People and Delivery Management

·       Lead, mentor and develop engineering teams with clear goals and accountability.

·       Own estimation, capacity, execution, dependencies, risks, releases and delivery recovery plans.

·       Conduct regular one-to-ones, performance reviews, coaching and skill-development planning.

·       Work with Product, QA, DevOps and stakeholders to deliver measurable product outcomes.

3. AI Project Execution

·       Assess AI use cases for feasibility, data readiness, risk, cost and measurable value.

·       Lead delivery of LLM, RAG, agentic and AI-assisted product initiatives from PoC to production.

·       Ensure evaluation, human validation, security, privacy, observability and rollback controls.

·       Prevent isolated demos, uncontrolled AI usage and dependency on individual engineers or vendors.

Required Skills and Experience

·       10+ years in software engineering with significant technical and people leadership experience.

·       Strong understanding of Python/FastAPI, React/Next.js, APIs, databases, cloud and distributed systems.

·       Proven ownership of product delivery, project planning, risk management and production operations.

·       Experience hiring, mentoring, evaluating and developing engineering teams.

·       Strong knowledge and execution experience across AI/ML, LLM, RAG or agentic-AI projects.

·       Practical understanding of Generative AI, LLMs, RAG, Agentic AI and responsible AI use relevant to the role.

·       Ability to use approved AI tools productively while protecting source code, credentials, learner data and organisational information.

·       Commitment to human review, testing, security validation and traceability for AI-assisted outputs.

Good-to-Have Experience

·       EdTech or medical-learning product experience.

·       AWS, Kubernetes, DevSecOps, MLOps or LLMOps.

·       Experience scaling teams and platforms through major transformation.

How Success Will Be Measured

·       Delivery becomes predictable with risks identified early.

·       Engineering quality, security and production stability improve.

·       AI projects progress from validated use cases to reliable production outcomes.

·       Team capability, ownership, engagement and succession strength increase.

·       Work consistently delivers measurable outcomes and business value—not merely completion of assigned tasks or activities.

Expected mindset: Own the outcome, use AI responsibly, communicate risks early and deliver secure, measurable value for DocTutorials and its learners.

Solid product experience and an educational background from Tier‑1 universities are preferred.

As a Senior AI Engineer, you will play a key role in designing and delivering highly scalable, next generation AI products within our AI stack. You will work in a technically rich environment with talented peers, contributing to product planning, design, and execution while influencing the evolution of Teradata’s AI capabilities. 

In this role, you will: 

• Design, develop, and maintain high quality systems, services, and applications with an emphasis on scalability, reliability, and performance. 
• Collaborate with cross-functional engineers and product partners to shape design and consistently deliver end to end features. 
• Build and integrate robust RESTful APIs, ensuring security, data consistency, and maintainability. 
• Work with SQL and NoSQL databases to implement efficient data models and service access patterns. 
• Apply and experiment with AI/ML technologies, including agentic AI and large language models (LLMs). 
• Use AI powered engineering tools to improve development quality, speed, and productivity. 
• Mentor engineers, supporting them in technical planning, implementation, and best practices. 
• Identify and resolve system performance bottlenecks, optimizing code, design, and infrastructure. 
• Write unit and integration tests and participate in code reviews to uphold engineering excellence. 
• Investigate production issues, ensuring timely and effective solutions. 
• Stay current with emerging trends in engineering, AI/ML, and cloud-native technologies. 

Who You’ll Work With 

You will be part of a high-performing engineering team building core components of Teradata’s AI platform. You will collaborate closely with other engineers, AI/ML specialists, product managers, cloud architects, and partner teams across the company. The role offers opportunities to contribute directly to Teradata’s AI product roadmap and technological direction. 

What Makes You a Qualified Candidate 

(Nonnegotiable qualifications) 

• Bachelor’s degree in Computer Science or equivalent from a recognized institution. 
• 5+ years of experience in backend services, distributed systems, or data platform development. 
• Strong proficiency in Java, Go, or Python for service development. 
• Good understanding of design principles, distributed system patterns, and service framework. 
• Hands-on experience designing and developing RESTful APIs. 
• Experience with SQL and NoSQL databases and data modelling. 
• Strong debugging, problem solving, and troubleshooting skills. 
• Experience with modern containerization and orchestration tools such as Kubernetes. 
• Knowledge of public cloud platforms. 
• Experience with AI productivity tools (e.g., GitHub Copilot). 
• Ability to work independently, drive workstreams, and deliver high quality outcomes. 
• Strong communication and collaboration skills. 

What You’ll Bring 

(Preferred qualifications) 

• Familiarity with messaging systems such as Kafka. 
• Experience experimenting with or implementing AI/ML technologies, Agentic AI, or LLMs. 
• A track record of influencing design decisions and driving engineering best practices. 
• The ability to mentor engineers and guide them through design, planning, and execution. 
• A proactive mindset and comfort working in fast paced, dynamic environments. 
• Curiosity, adaptability, and a passion for building impactful AI-driven products. 

Technical Architect – Product Engineering

Experience: 15+ Years

Location: Pune, India

Employment Type: Full-time

Desired Skills: Python, Technical Architecture, AWS, Microservices, SaaS / Multi-tenant Architecture, Kubernetes, System Design

About the Role

We are looking for a Senior Technical Architect to lead the architecture, design, and technical evolution of an enterprise SaaS product. This is a hands-on leadership role requiring deep technical expertise, strong product engineering experience, and the ability to build scalable, secure, and high-performance platforms.

The ideal candidate should be passionate about solving complex engineering problems, driving innovation, mentoring development teams, and effectively leveraging AI to accelerate software development.

Key Responsibilities

• Own the overall product architecture and technical roadmap.
• Design and build scalable, secure, and highly available enterprise applications.
• Lead the design and implementation of new product features from concept to production.
• Remain hands-on with coding and contribute to critical product components.
• Drive architecture reviews, code quality, performance optimization, and engineering best practices.
• Lead cloud architecture, security, scalability, and DevOps initiatives.
• Evaluate and adopt modern technologies to improve product capabilities and engineering efficiency.
• Leverage AI tools (ChatGPT, GitHub Copilot, Cursor, Claude, etc.) to accelerate software development, code reviews, testing, documentation, debugging, and productivity.

Required Skills & Qualifications

• 15+ years of software product engineering experience with at least 5 years in a Technical Architect role.
• Strong hands-on expertise in Python and modern backend frameworks.
• Deep experience with AWS services and cloud-native application architecture.
• Strong understanding of DevOps, CI/CD pipelines, Infrastructure as Code (Terraform/CloudFormation), Docker, Kubernetes, and container orchestration.
• Experience designing microservices, REST APIs, event-driven architectures, and distributed systems.
• Strong knowledge of SQL and NoSQL databases.
• Experience with scalable SaaS platforms, multi-tenant architectures, and secure application design.
• Excellent understanding of software design patterns, performance tuning, observability, and system reliability.
• Strong analytical, problem-solving, and decision-making skills.

AI Architect – Enterprise AI & Generative AI ( Remote) - Contractual

Experience: 8+ years

Location: Remote, India

Employment Type: Contractual

Working Hours: 3:00 PM – 12:00 AM IST

Client Engagement: US-based client

About the Role

We are looking for a hands-on, client-facing AI Architect to lead the architecture, technical direction, and production implementation of enterprise AI and Generative AI solutions for a US-based client.

You should combine strong software engineering and cloud architecture fundamentals with practical experience building secure, scalable, and reliable AI applications. This role involves end-to-end technical ownership, from solution design and prototyping to production deployment and optimization. It is not a research-only, advisory-only, or prompt-writing position.

Key Responsibilities

• Lead end-to-end architecture and delivery of AI/ML and Generative AI solutions, from discovery and proof of concept to production rollout.
• Design LLM-powered applications using Retrieval-Augmented Generation (RAG), embeddings, vector databases, AI agents, and orchestration frameworks.
• Define data ingestion, chunking, retrieval, grounding, evaluation, and knowledge-refresh strategies.
• Select appropriate AI models and platforms based on quality, latency, cost, security, and operational requirements.
• Design cloud-native architectures, APIs, integrations, identity controls, monitoring, and resilient deployment patterns.
• Create architecture diagrams, technical designs, implementation roadmaps, and technical standards.
• Implement AI quality and safety measures, including hallucination mitigation, guardrails, access controls, and prompt-injection defenses.
• Guide engineering, data, ML, and DevOps teams through implementation, code reviews, troubleshooting, and go-live readiness.
• Lead technical discussions and design reviews with US client stakeholders.
• Establish production monitoring, CI/CD, versioning, incident handling, and performance and cost optimization practices.

Required Skills & Qualifications

• 8+ years of relevant experience in software engineering, AI/ML engineering, data engineering, or technology architecture.
• Proven experience personally architecting and deploying at least one enterprise-grade AI or Generative AI solution into production.
• Strong practical knowledge of LLMs, RAG, embeddings, vector databases, orchestration frameworks, and model evaluation.
• Hands-on experience with Python and APIs, including prototyping, code review, and troubleshooting.
• Experience with at least one major cloud platform: Microsoft Azure, AWS, or Google Cloud Platform (GCP).
• Understanding of data pipelines, SQL/NoSQL databases, containers, CI/CD, MLOps, and LLMOps.
• Knowledge of enterprise security, privacy, identity and access management, and responsible AI practices.
• Experience independently leading architecture workshops, design reviews, and technical discussions with US clients.
• Excellent written and spoken English, documentation, and stakeholder management skills.
• Availability to work from 3:00 PM to 12:00 AM IST and willingness to accept a contractual engagement.

Preferred Experience

• Azure AI Foundry, Azure OpenAI, Amazon Bedrock, or Google Vertex AI.
• Azure AI Search or equivalent retrieval tools.
• LangChain, LangGraph, Semantic Kernel, and agent/tool orchestration.
• Model fine-tuning, predictive ML, document intelligence, multimodal AI, or real-time AI applications.
• Experience in cloud modernization, enterprise integrations, data analytics, or technology consulting.
• Relevant AI/cloud architecture certifications and experience mentoring technical teams.

What Success Looks Like

• Designing secure, scalable, production-ready AI solutions rather than demos or conceptual presentations.
• Making sound technical decisions around feasibility, performance, reliability, cost, and AI risks.
• Taking ownership of architecture decisions and resolving technical blockers with limited supervision.
• Building trust with senior US stakeholders and collaborating effectively with India-based delivery teams.

Contract Details

• Initial engagement: Minimum 3 months, with extension subject to project requirements and performance.
• Working schedule: 3:00 PM – 12:00 AM IST.
• Potential permanent employment: After 18 months, the client may consider direct permanent employment at its sole discretion. Conversion is not guaranteed.

Mandatory Screening Criteria

Candidates should be able to:

• Explain an enterprise AI/GenAI solution they personally architected and deployed, including architecture, scale, evaluation, and business impact.
• Whiteboard an enterprise RAG or agentic solution covering data flow, security, authentication, authorization, monitoring, and cost controls.
• Discuss real-world model-quality issues, hallucinations, privacy risks, incidents, and architectural trade-offs.
• Lead a technical design review with US client stakeholders.
• Confirm acceptance of the working hours and contractual engagement.

Apply only if you have hands-on experience architecting and deploying enterprise AI/GenAI solutions in production.

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.

Looking for an Architect with strong experience in AI and Data Engineering, preferably with hands-on experience in Generative AI (GenAI). Candidates with experience in any cloud platform (AWS, Azure, or GCP) will be considered, 

About the Company

The client is revolutionising the way businesses operate through cutting-edge technological solutions. Their focus is on developing intelligent agents and agentic workflows that automate processes and eliminate the need for human effort wherever possible. By leveraging

advanced AI and machine learning, they create systems that enhance productivity and drive efficiency.

Their expertise extends to the fintech, healthcare and medical technology sectors, where they develop innovative solutions that improve patient outcomes and streamline medical operations.

From medical devices to healthcare platforms, their work sits at the intersection of technology and medicine, pushing the boundaries of what's possible. The team is dedicated to continuous learning and growth, ensuring the team members are always at the forefront of the tech landscape.

About the Role

This is a senior, hands-on engineering role at the heart of our product team. You will be one of the most technical people in the room — setting the architecture for our real-time voice AI

agents and building the hardest parts of it yourself. From the systems that power live conversations to the interfaces our clients rely on, you will own how the product is engineered end to end.

We are looking for a genuine lead full-stack engineer with the depth to make architecture decisions that hold up as we scale, and the appetite to still be in the code every day. You should be as comfortable designing the backend services behind a live voice agent as you are shaping a clean interface on top of them — and comfortable being the person others turn to when something is hard.

You will work directly with the founder and product leadership on a fast-moving product, with real influence over technical direction. This is a role for someone who wants ownership at the level of "how the whole thing is built," not just individual features — and who raises the bar for

everyone around them.

What You'll Own

Set the technical direction

• Own the architecture of our core systems — the real-time voice agents, backend
• services, data and APIs — making the decisions that keep the product fast, reliable and scalable as it grows.
• Lead the hardest engineering problems and solve them personally.
• Establish engineering standards — code quality, review practices, testing and technical patterns that the team builds to.
• Drive technical strategy with the founder and product leadership — shaping the roadmap, flagging risk early, and turning product ambition into a sound technical plan.

Build the product end to end

• Design, build and ship features across the stack — backend services, APIs and front-ends — owning them from idea to production.
• Build the client-facing surfaces — dashboards, review tools and configuration interfaces that let our clients run and trust the product.
• Design and evolve the data models and APIs that hold up as we scale across clients.

Make it reliable and fast

• Own production quality — put the monitoring and alerting in place so issues are caught before clients feel them, and performance stays within target.
• Care about performance — find and fix bottlenecks across the stack.
• Build for correctness — put the testing and evaluation in place that keeps the product behaving predictably as it changes.

Lead through the team

• Mentor and grow engineers — through code review, pairing, and setting a technical example others learn from.
• Multiply the team's output — unblock others and lift the overall quality of the codebase.
• Take features from ambiguity to done — turn a rough product goal into a shipped, working capability with minimal hand-holding, and help others do the same.

What We're Looking For

• 8+ years of professional software engineering experience, with significant depth across backend and a track record of owning systems, not just features.
• Strong backend engineering, ideally in Python — building and scaling production services and APIs..
• Proven architecture and system-design ability — you have designed systems that scaled, and can reason clearly about trade-offs.
• Solid fundamentals across APIs, databases and cloud infrastructure.
• Experience building real-time and/or AI-powered products — or clear, demonstrable ability to lead in this area.
• A history of technical leadership — setting standards, mentoring engineers, and being trusted with the hardest problems — while remaining hands-on.
• Excellent communication and a genuine ownership mindset — someone who can be handed an ambiguous, high-stakes problem and be trusted to see it through.

Nice to Have

• Experience working with AI / large language models in production.
• Experience with voice or other real-time products.
• Exposure to healthcare, fintech, or other regulated / high-stakes domains.
• Experience as an early or senior engineer in a startup, where you set direction and wore many hats.