Job Description :

- Build LLM-powered document understanding features extracting structured, reliable data from unstructured enterprise documents

- Own AI feature UIs end-to-end you build the interface, not just the model integration layer

- Design and maintain an eval framework define what 'working' means for each AI feature and catch regressions before users do

- Drive model selection and integration decisions choosing the right provider and approach for each use case, managing latency and cost

- Own AI platform reliability observability, fallback behaviour, and graceful degradation when models fail

- Work closely with product, customer success, and the full-stack engineer AI features only matter if they are usable and trusted by real users

THE IMPACT YOU'LL MAKE :

- You will define what AI means the features you ship will be the most visible and differentiated parts of the product

- CS Copilot, if done well, changes how enterprise customer success teams operate every single day this is a high-stakes, high-visibility surface

- You will establish the engineering culture around AI reliability evals, observability, and disciplined iteration

- Your work will directly accelerate enterprise deals AI features are increasingly a buying criterion for our clients

- You will be the person who brings engineering rigour to a domain where most companies ship demos and call it a feature

Must-Haves :

- Must have minimum 5+ years of total software development experience, with at least 2+ years working on Gen AI / AI / LLM-based features in production

- Must have strong backend engineering experience using Python (FastAPI / Django preferred) and building production-grade systems

- Must have hands-on experience building LLM-based applications, including OpenAI / Gemini / similar models in real projects

- Must have experience with RAG (Retrieval Augmented Generation) including chunking, embeddings, and retrieval pipelines

- Must have experience designing end-to-end AI pipelines, including chaining, tool usage, structured outputs, and handling failure cases

- Must have experience building agentic AI systems (multi-step workflows, tool orchestration like LangGraph / CrewAI or custom agents)

- Must have strong coding and system design skills, not just prompt engineering or experimentation

- Must have experience shipping AI features in production, not just POCs or research projects

- Must have experience working with APIs, backend services, and integrations

- Must have understanding of AI system reliability, including latency, cost optimization, fallback handling, and basic eval thinking

- Product companies / startups, preferably Series A to Series D

- Mandatory (Note) - Candidate's overall experience should not be more than 7 Yrs

- Mandatory (Tech Stack) Strong in Python + AI/LLM ecosystem, experience with modern AI tooling and frameworks