The role

As a Senior AI Engineer, you'll own the design and delivery of the AI systems at the core of Epistak's product. You'll take LLM-powered features from prototype to reliable production service, set the bar for how we evaluate model quality, and help shape our engineering culture and early hiring. This is a hands-on individual contributor role reporting to the Founding Architect and the Founder.

What you'll do

- Design, build and ship production AI features using large language models, retrieval-augmented generation (RAG) and agentic workflows.
- Build agents that connect to enterprise IT and security systems, unify their data into shared context, and reason across it for root-cause analysis, patch prioritization and alert triage
- Make every conclusion explainable, with the evidence behind it, and design automated actions that stay within defined policies and approvals
- Own the architecture of our AI stack: model selection, orchestration, retrieval, caching and guardrails
- Build evaluation pipelines (offline test sets, LLM-as-judge, human review loops) and use them to drive decisions
- Monitor models in production for quality, latency, cost and drift, and fix issues before customers notice
- Fine-tune or adapt models where off-the-shelf APIs fall short
- Work with product and design to turn ambiguous problems into well-scoped AI solutions
- Review code and design docs, and set the technical bar for the engineers who join after you
- Keep up with the field, spot emerging patterns and techniques, and bring them into the product when they make a real difference





What you bring The ideal candidate started in data engineering (analytics and data modelling), moved into ML engineering, and has built LLM-based workflows since GPT arrived. That path gives you a feel for the data, the models and the workflows together.

- A data engineering foundation: strong SQL, data modelling, and building pipelines on warehouses or lakes (e.g. Snowflake, BigQuery, Databricks)
- Hands-on experience shipping LLM-based workflows: prompt design, RAG, tool use, agents and multi-step pipelines
- Strong machine learning fundamentals: supervised learning, embeddings, model evaluation metrics, overfitting and bias, and when a classical model beats an LLM
- Solid Python, plus experience with PyTorch, Hugging Face, scikit-learn or similar
- Experience with vector databases and search (e.g. pgvector, Pinecone, Weaviate, Elasticsearch)
- A rigorous approach to evaluation — you measure quality rather than eyeballing it
- Solid backend fundamentals: APIs, data pipelines, testing, CI/CD
- Experience deploying on a major cloud (AWS, GCP or Azure) with Docker and Kubernetes

Nice to have

- Startup experience
- Experience with knowledge graphs or other ways of modelling relationships between entities
- Experience with IT operations or security data (ITSM, SIEM, CMDB, vulnerability scanners)
- Experience fine-tuning models (LoRA, RLHF/DPO) or training smaller task-specific models
- Background in MLOps tooling: experiment tracking, model registries, feature stores
- Experience optimizing inference (quantization, batching, serving with vLLM or similar)
- Publications, open-source contributions or a portfolio of AI projects

📌 Senior AI Engineer (Bengaluru)
🏢 Epistak.ai
📍 Bengaluru