We are looking for an AVP — Engineering (or Engineering Manager) who is hands-on, has built and shipped GenAI-driven systems, and can lead a small but high-leverage engineering team.
This is not a pure people-management role. You will spend a meaningful portion of your week writing Python, designing AI workflows, reviewing code, debugging vector retrieval issues, and shaping system architecture. You will also own the engineering rhythm of the team — sprint planning, code quality, hiring, mentoring, and delivery commitments to clients.
You will report to senior leadership and partner closely with product and client-facing teams. You will work across multiple concurrent client builds, so prioritisation, technical judgement, and the ability to keep things simple are essential.
Experience: 6+ years
Location: Pune (On-Site)
What You'll Do-
Hands-on Engineering:
- Build and own end to end backend services in Python (FastAPI / Flask) — APIs, data pipelines, async workers, integrations with ERPs, accounting systems, and document stores.
- Design and implement AI / GenAI workflows — RAG pipelines, agentic workflows, multi-step LLM orchestration, structured extraction from documents (invoices, contracts, financial statements, audit evidence).
- Make framework and model choices across LangChain / LlamaIndex / LangGraph, OpenAI / Anthropic / open-source models, and vector stores (pgvector, Pinecone, Weaviate, Chroma) — and justify them with real trade-offs.
- Write production-grade code with proper testing, observability, error handling, and cost controls around LLM calls.
- Architect for scale and reliability — microservices, queues, caching, rate-limiting, database design (Postgres, MongoDB), and cloud deployment (AWS / Azure / GCP).
- Own evaluation and quality of AI outputs — design eval harnesses, prompt regression tests, golden datasets, and feedback loops with subject-matter experts.
AI & GenAI Leadership:
- Set the technical direction for how we build AI features — patterns, guardrails, and reusable components across products.
- Stay current with the GenAI ecosystem (new models, frameworks, agentic patterns, evaluation tooling) and translate that into pragmatic decisions for the team.
- Build internal accelerators — shared libraries, prompt registries, evaluation tooling — that compress build time across client engagements.
- Partner with a product on what is feasible, what is risky, and what is genuinely differentiated.
Engineering Management:
- Lead a team of 4–8 engineers across backend, AI, and full-stack, with growth as the team scales.
- Run the engineering cadence — sprint planning, standups, retros, code reviews, and release management.
- Set quality bars: coding standards, PR discipline, testing expectations, documentation, and on-call practices.
- Coach and grow engineers — career conversations, technical mentoring, performance feedback.
- Hire well — define roles, run technical interviews, and build a strong engineering brand for us. .
- Be the engineering point-of-contact for client technical conversations — solution design discussions, architecture reviews, security and compliance questions.
Must-Have Qualifications:
- 6+ years of professional software engineering experience, with a strong record of shipping production systems.
- Deep, hands-on Python backend experience — FastAPI or Flask, async programming, REST API design, SQL and ORM (SQLAlchemy or similar).
- 2+ years of meaningful GenAI / LLM application experience — not just prototypes. You have shipped something real that used LLMs at its core.
- Strong working knowledge of RAG architectures, embeddings, vector databases, and prompt engineering patterns.
- Experience with at least one major LLM ecosystem (OpenAI, Anthropic, Azure OpenAI, AWS Bedrock) and at least one orchestration framework (LangChain, LlamaIndex, LangGraph, or equivalent).
- Solid system design fundamentals — databases, caching, queues, microservices, API design, authentication.
- Cloud experience on AWS, Azure, or GCP — at least the core compute, storage, networking, and managed database services.
- Experience leading engineers — formally as a manager, or informally as a tech lead with ownership over team output.
- Clear, structured written and verbal communication. You can explain a system to an engineer, a product manager, and a client CFO.
Why Us:
- Built at the frontier — every product we ship is GenAI-native, not GenAI-bolted-on.
- Real problems, real customers — large airlines, manufacturing cos, and audit firms with genuine pain points.
- Senior-level ownership — you will shape the engineering org, not inherit a fixed playbook.
- Competitive compensation, including ESOPs, with real upside as the company scales
Interested candidates can share their resumes at [Confidential Information] or DM directly for more details.
