CTO- Healthcare

About the company

We are an AI diffusion company. We partner with midmarket companies in asset-heavy, regulated industries to co-build deeply industry-specific agentic and machine learning solutions, which then get diffused across the industry through revenue-share and equity-linked models.

Our Past

We have built and sold two companies, one of which was an AI company. Our founders and leaders are Math PhDs, Ivy League University Alumni, Ex-Googlers, and successful entrepreneurs.

Team

Our team of 20+ employees consists of data scientists, AI/ML Engineers, and mathematicians from top engineering and research institutes such as IITs, CERN, IISc, UZH. Our team includes academicians, IBM Research Fellows, and former founders. This role focuses on healthcare and RCM, working alongside our AI/ML engineers, data scientists and domain specialists.

Work you’ll do

We are looking for an experienced AI engineer with hands-on experience in healthcare and revenue cycle management (RCM). You will build and deploy AI agents, automations and data workflows for healthcare operations, with a focus on the US market.

The work includes payer-portal automation for claim status and EOB retrieval, voice agents for payer follow-up, analysis of claim journeys and bottlenecks, and AI-assisted insights into practice performance. You will work directly with billing and operations teams, owning delivery from understanding the workflow through deployment, evaluation and ongoing improvement.

Responsibilities

• Own AI agent and automation workflows from requirements through design, development, deployment and monitoring.
• Build agents that work across payer portals, healthcare documents, practice-management systems, EHR-connected tools and voice channels.
• Turn fragmented healthcare data into validated, structured inputs for automation and analysis.
• Work directly with billing and operations teams to understand workflows, resolve exceptions and improve systems based on user feedback.
• Evaluate workflow accuracy and completion rates using representative test sets. Monitor failures and operating costs, and implement retries, exception queues and human escalation
• Implement approved privacy and security controls for sensitive healthcare data, including access restrictions, audit trails and safeguards against exposing patient information in logs or model inputs.
• Diagnose and improve failures across data ingestion, integrations, agent execution and user-facing workflows.
• Measure operational outcomes, such as time saved, reduced manual effort and faster turnaround, with the teams using the systems.

Who you are

You are a passionate and results-oriented engineer who is driven by the potential of AI/ML to revolutionize processes, enhance products, and ultimately improve user experiences. You thrive in dynamic environments and are comfortable navigating ambiguity. You possess a strong sense of ownership and are eager to take initiative, advocating for your technical decisions while remaining open to feedback and collaboration.

You are adept at working with real-world, often imperfect data, and have a proven ability to develop, refine, and deploy AI/ML models into production in a cost-effective and scalable manner. You are excited by the prospect of making US healthcare operations faster and more reliable, for the people who run them and the patients they serve.

Skills & Requirements

Must have

• At least 2 years of professional AI engineering experience, including deploying and supporting systems used in real operations. Internships and academic projects alone do not meet this requirement.
• Experience deploying and supporting an LLM-based application or AI agent in production, using capabilities such as retrieval-augmented generation (RAG), tool calling or multi-step workflows.
• Preferably has hands-on experience building AI or automation solutions for healthcare RCM workflows, such as claim status checks, accounts receivable follow-up, denial management, payment posting or payer interactions.
• Preferably has practical understanding of how billing teams work, how claims move through the revenue cycle, and how incomplete data and exceptions affect execution.
• Ability to explain your engineering contribution, how the system was evaluated, what failed and how you addressed it.
• Strong production Python and SQL skills, experience integrating APIs, and working knowledge of JavaScript/TypeScript for browser automation or application integration.
• Experience automating workflows across APIs, browsers or user interfaces, using tools such as Playwright, Selenium or UiPath, with error handling and human escalation where needed.
• Experience deploying and monitoring applications on at least one cloud platform—AWS, Azure or GCP—using containers, version control and automated testing.
• Experience handling imperfect inputs such as scanned PDFs, inconsistent portal layouts or incomplete records, with validation and exception handling.
• Experience working directly with billing or operations teams, improving systems based on their feedback, and demonstrating outcomes such as time saved, claims processed or reduced turnaround time.
• Experience handling sensitive healthcare data through appropriate access controls, secure logging and approved data-handling practices.

Preferred experience

• Experience delivering solutions for US healthcare providers, RCM companies or payer-facing operations, including working with US billing teams and payer workflows.
• Experience with practice-management or EHR integrations, payer portals, explanation of benefits (EOB) documents or electronic remittance advice (ERA) data.
• Experience analyzing claim histories, revenue-cycle bottlenecks or practice performance.
• Eperience building voice agents or integrating speech-to-text and text-to-speech services is a plus.
• Familiarity with Model Context Protocol (MCP) integrations.
• Experience with Kubernetes, serverless applications or event-driven architectures.

Working with us

We are a young company, but we believe strongly in thinking long-term, while acting with urgency. Our ethos is rooted in innovation, efficiency and high-quality outcomes. We believe the future of work is AI-augmented and boundaryless.

Here are some of our guiding principles:

• Think in decades. Act in hours. As an independent company, our moat is time. While our decisions are for the long-term horizon, our execution will be fast – measured in hours and days, not weeks and months.
• Own the canvas. Throw yourself in to build, fix or improve – anything that isn’t done right, irrespective of who did it. Be selfish about improving across the organization – because once the rot sets in, we waste years in surgery and recovery.
• Use data or don’t use data. Use data where you ought to but not as a ‘cover-my-back’ political tool. Be capable of making decisions with partial or limited data. Get better at intuition and pattern-matching. Whichever way you go, be mostly right about it.
• Avoid work about work. Process creeps on purpose, unless we constantly question it. We are deliberate about committing to rituals that take time away from the actual work. We truly believe that a meeting that could be an email, should be an email and you don’t need a person with the highest title to say that out loud.
• High revenue per person. We work backwards from this metric. Our default is to automate instead of hiring. We multi-skill our people to own more outcomes than hiring someone who has less to do. We don’t like squatting and hoarding that comes in the form of hiring for growth. High revenue per person comes from high quality work from everyone. We demand it.

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.

Technogise provides software engineering services. We build software for clients who buy the quality of the work and business outcomes rather than the headcount. Over the last year, we built an AI-augmented way of delivering it and won business on that basis. This role grows that business and keeps the capability behind it ahead of what we offer.

Why this role exists

You’ll use your technology and engineering expertise to grow the business and strengthen the organisation’s capabilities, with AI at the core.

This includes marketing, outreach, account growth, and hiring.

You’ll execute, support teams, and improve processes wherever your expertise can make the biggest impact.

What you will own: growing the business

Pre-sales and solutioning. Work with the sales side on accounts in the presentation and proposal stage: understand the customer's problem, shape the technical solution, size it, and present it. You will be in front of customers, and you need to hold a technical conversation with them without a manager translating.

Accounts and the commercial side. You need to understand how a proposal becomes a staffed team, what bench and utilisation cost, why a delivery slip is a commercial problem before it is a technical one, and how an account grows from one team to three. You will be asked for a view on pricing and staffing shape, not only on architecture.

Team Readiness & Capability Building. Drive cross-technology training and forward-looking learning initiatives to prepare teams for emerging technologies, AI adoption, and evolving client needs.

Thought Leadership & Sales. Drive technical thought leadership through case studies, deep dives, and blogs that showcase our expertise and build credibility. Translate this content into marketing, lead generation, and sales opportunities by helping prospects understand how we solve their challenges. Use AI to scale content creation while ensuring technical depth, quality, and business relevance.

Hiring. Take the interviews where the technical call is hard, keep the bar aligned with the work we are selling now, and give the recruitment team a way to read a candidate's AI claims that is better than counting tool names on a resume.

What you will own: Raising what we can do

Capability and enablement. Keep our AI-augmented engineering practice up to date and drive its adoption through maturity assessments, training, hands-on team support, and evaluation of new tools. Help engineers, recruitment, and people teams understand and build the AI capabilities we need..

Capability we can sell. Turn things we can do into things we can offer. A new service line starts as an internal spike, becomes a demo, then a proposal. Recent examples are agentic AI engagements, infrastructure automation, and data engineering.

Technical cover on delivery. Support multiple active engagements by helping delivery leads resolve tough design decisions, validate estimates, review code and architecture, and address quality issues early. Stay independent of individual teams to provide an objective, cross-project perspective. Champion AI while ensuring the focus remains on delivering real outcomes.

What we are looking for

• Genuinely strong hands-on engineering. You still read and write code, and you can hold your own in a design review with the sharp engineers in the room. This one is not negotiable.
• An instinct for the business of a services firm: proposals, staffing, utilisation, and how repeat business actually happens.
• Hands-on with LLM-based development: coding agents, agentic workflows, retrieval over private data, and judging what a model actually produced
• Comfortable in front of a customer, including one who is technical and sceptical
• A teaching instinct. You like making other engineers better and you have evidence of having done it.
• Judgement about quality. You can tell the difference between a demo that works and a system that will hold
• Able to work through functions you do not run, since marketing, recruitment and sales own their own work and you make it sharper
• Comfortable being measured by business you helped win and capability you raised, rather than by software you personally shipped

Useful but not required: exposure to data engineering, a public body of writing or open-source work, experience running training for engineers.

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.

Director of Engineering (AI Solutions & Delivery)

About the Role

We are looking for a Director of Engineering to lead engineering execution and delivery across our enterprise AI solutions. This role combines strong engineering leadership with operational discipline, ensuring AI products move from technical design to production with predictable timelines, high quality, and reliable performance.

You will lead Engineering Managers and Tech Leads, oversee multiple engineering teams, and drive delivery excellence across AI-powered applications and platforms. The focus is on execution, technical quality, and organizational accountability, ensuring teams deliver scalable, production-ready AI systems without compromising reliability or engineering standards.

Key Responsibilities

1. Engineering Delivery & Execution

• Own engineering delivery schedules, translating product roadmaps into achievable sprint plans and release milestones.
• Establish measurable delivery targets, aiming for at least 85% on-time delivery across engineering teams.
• Identify and resolve cross-team dependencies, technical bottlenecks, and resource constraints before they affect delivery.
• Drive engineering process improvements across Agile development, CI/CD, automated testing, and release management.
• Monitor delivery metrics, sprint commitments, and release readiness to maintain predictable execution.

2. AI Quality, Evaluation & Governance

• Establish automated AI evaluation pipelines to measure hallucinations, relevance, groundedness, and contextual accuracy before production releases.
• Define quality benchmarks and evaluation criteria for LLM-powered applications and AI-driven workflows.
• Implement safety mechanisms, validation filters, circuit breakers, and human-in-the-loop fallbacks to manage unpredictable AI outputs.
• Oversee RAG pipelines, vector databases, data ingestion, and retrieval mechanisms to maintain accurate, reliable, and compliant context.
• Reduce production AI quality regressions through continuous evaluation, monitoring, and feedback loops.

3. Architecture, Scalability & Reliability

• Oversee the architecture of distributed systems and cloud-native applications to support scalability, availability, and performance.
• Drive reliability targets, including 99.9% uptime and low time-to-first-token (TTFT) latency for applicable AI applications.
• Partner with Staff and Principal Engineers to design resilient systems, including fallback mechanisms for model API failures, timeouts, and rate limits.
• Establish engineering standards for code reviews, observability, automated integration testing, and security compliance.
• Ensure production systems meet defined service-level objectives (SLOs) and performance benchmarks.

4. Engineering Leadership & Team Development

• Lead and develop Engineering Managers and Tech Leads, establishing clear ownership, accountability, and delivery expectations.
• Build a high-performance engineering culture focused on technical excellence, collaboration, and continuous improvement.
• Partner with Talent Acquisition to attract, assess, and hire strong software engineering and AI talent.
• Align engineering resources with business priorities, delivery commitments, and technical requirements.
• Address performance and execution challenges while maintaining team engagement and engineering quality.

Success Metrics: First 90 Days

First 30 Days

• Assess existing engineering workflows, delivery pipelines, and release processes.
• Establish baseline AI evaluation criteria and identify gaps in production quality.
• Take ownership of engineering delivery across teams and align expectations with Engineering Managers and Tech Leads.

First 60 Days

• Implement automated AI evaluation frameworks and establish measurable quality benchmarks.
• Improve cross-team coordination and resolve key delivery dependencies.
• Establish consistent sprint planning, release cadences, and delivery reporting.

First 90 Days

• Achieve at least 85% sprint commitment completion.
• Demonstrate measurable reductions in production AI quality regressions.
• Improve system reliability and stabilize latency against defined performance targets.
• Establish clear accountability and predictable delivery practices across engineering teams.

Qualifications & Requirements

• Experience: 14+ years in software engineering, including at least 5 years managing Engineering Managers or Tech Leads in enterprise or high-growth environments.
• AI/ML Expertise: Hands-on experience delivering AI-powered systems involving LLMs, RAG architectures, vector databases, prompt engineering, or model fine-tuning workflows.
• Engineering Delivery: Proven ability to lead multiple engineering teams and deliver complex technical initiatives from design through production launch.
• System Architecture: Experience with distributed systems, cloud-native backend infrastructure, and scalable APIs across AWS, GCP, or Azure.
• AI Quality & Reliability: Understanding of AI evaluation frameworks, production monitoring, hallucination mitigation, guardrails, and system reliability.
• Delivery Management: Strong experience with Agile methodologies, CI/CD, release planning, and engineering performance metrics.
• Leadership: Ability to establish accountability, manage competing priorities, and deliver against ambitious deadlines without compromising quality or team morale.

What Success Looks Like

The successful candidate will establish predictable engineering delivery, improve the quality and reliability of production AI systems, and build a culture of ownership across engineering teams. They will combine technical judgment with execution-focused leadership to ensure AI solutions are delivered on schedule, operate reliably, and meet enterprise expectations.

If you are passionate about building high-performing engineering teams and delivering enterprise-grade AI solutions at scale, we would like to hear from you.

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.

Key Skills:

Python & System Design: Strong expertise in Python and system design, with proven experience architecting scalable, production-grade systems.

LLM & Agentic AI: Proven experience building LLM-based and agentic systems from the ground up and evaluating them based on reproducibility, calibration, and cost.

Cloud & Data Architecture: Deep understanding of data and cloud architecture across AWS and Azure, including multi-store data design, distributed systems, streaming, security, and cost optimization.

Azure Databricks: Hands-on production experience with Azure Databricks architecture, including Unity Catalog, Delta Lake, cluster policy design, job orchestration, and Terraform provisioning.

Supply Chain & Inventory Management: Strong domain knowledge of supply chain and inventory operations, including demand planning, MRP logic, inventory parameters, and translating business planning decisions into data requirements.

System Architecture & Technical Decision-Making: Ability to make sound architectural decisions based on first principles, evaluate technical trade-offs, and avoid unnecessary complexity and abstraction.

LLM & Agentic AI: Proven experience building LLM-based and agentic systems from the ground up and evaluating them based on reproducibility, calibration, and cost.

Technical Leadership: Ability to influence teams without direct authority, establish technical standards, prepare technical specifications and documentation, and mentor team members.

Experience in pharmaceutical or regulated industries is highly valued.

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.

We're hiring Drumbeat's first Senior Product Engineer to help us go to the next level.

We're not first-timers. We're the team that founded Phrasee in 2015 and grew it to more than 100 people, customers around the world, and a >$100m exit. Lots of people made lots of money, including the founding employees.

With Drumbeat, our plan is to do it all again… except this time, bigger and faster.

What is Drumbeat?

Drumbeat (www.drumbeat.com) predicts your next best action on LinkedIn, then copilots your team to turn invites, DMs and posts into pipeline.

We launched in September 2025 and have raised a £2m seed round led by a well-known VC. We're building the core team around our cofounders (Parry, Jonny and Dan) and Mohit, our Engineering Manager and Phrasee's first engineer. You'll report to Mohit.

What you'll be building on

• Setlist: a short, ordered list of the right things for each person to do today.
• Radar: predicts who each rep should connect with now, not everybody who matches a job-title search.
• Riff and AuthorDNA: help people find something worth saying, and write it in their own voice.
• Campaigns, Conversations, Backstage and Analytics: team-wide activity, replies, company knowledge and reporting.

Under the hood: Django and PostgreSQL, background jobs on Redis, LLM workflows, statistical models that learn what works, and integrations with LinkedIn and CRMs. A lot of our code is written with AI coding agents, inside a strict engineering process.

How we think about engineering

1. "Human-first AI, not AI-first humans." AI agents write a lot of our code. They don't make our decisions. We want someone super smart without AI, and even smarter with it, who knows when the machine is blowing smoke.
2. "Spend 55 minutes on the problem." Understand what's actually wrong before you fix it.
3. "Fix the class, not the instance." Every bug is an example of something bigger. Find the pattern, and fix it everywhere it lives.
4. "A check must be able to fail." A test you've never seen go red proves nothing.
5. "You don't need a Ferrari to drive to the supermarket." Simplicity over cleverness, every time.

What you'll own

1. Technical architecture. How Drumbeat is built, so it holds up as we add customers, features and integrations.
2. Shipping features end to end. From Parry and Mohit's briefs, and turning Parry's MVPs into production features.
3. Foundations. Scale, resilience (safe retries, timeouts and fallbacks when outside services wobble), cost and refactoring.
4. What you build. Once it's live, it's yours: you maintain it and fix it.
5. The hardest bugs. The ones nobody else can crack.

Your experience

• 8+ years as an engineer, at a product company, preferably B2B SaaS.
• Time at a smaller company, so you know how to get stuck in.
• You've owned a system in production, not just added features to one.
• Senior Python and Django: Postgres, background jobs, correctness in production.
• System architecture and refactoring.
• You direct AI coding agents well, and explain trade-offs in plain English to people who aren't engineers.

Where you'll work

Delhi / NCR. You'll be in our Delhi office some days each week, alongside Mohit.

Pay

₹40–65 lakh CTC, plus equity that vests as the company hits revenue milestones.

This is probably not a job for most people. Don't apply if you want an easy job. Apply if you want a hard one: lots of responsibility, very little bureaucracy, plenty to figure out, and a genuine chance to shape what this company becomes… and make a bunch of money as a result.

If that sounds exciting, LET'S PARTY.

🚀 We’re Hiring | Data Scientist 🧠📊

Ready to turn data into real-world intelligence? Join us and work on exciting AI/ML & data-driven solutions!

🔹 Experience: 8+ Years

🔹 Must-Have Skills:

🐍 Python | 🤖 Machine Learning | ☁️ Cloud | 🧠 NLP | 📊 Data Visualization

📍 Location: Pune

💼 Work Mode: Work from Office

If you're passionate about Data Science, AI & solving complex business problems, we’d love to hear from you!

📩 Interested? Kindly text

#Hiring #DataScientist #DataScience #MachineLearning #Python #NLP #AI #Cloud #DataVisualization #TechJobs #HiringNow