AI Engineer

AI Engineer

Role summary :

Design and ship AI-powered product features (LLMs, RAG, agents, ML APIs) into our existing services, working closely with backend, frontend, and data science teams.

You will :

- Integrate off the shelf and inhouse models (LLMs, embeddings, ML APIs) into robust microservices and user facing flows.

- Design and implement RAG and workflow/agent pipelines: retrieval, context assembly, tools integration, guardrails, and fallbacks.

- Hands-on with any of one Agent framework like Langchain, Langgraph, adk etc.

- Own AI service reliability in production: latency, throughput, cost, observability, circuitbreakers, and rollback/versioning of models and prompts.

- Collaborate with Data Scientists who own model training/finetuning and evaluation design; productionize their outputs as stable APIs/workflows.

- Implement logging, feedback capture, and lightweight online evaluation hooks to measure quality of AI features over time.

- Ensure safety, security, and compliance for AI features : prompt injection defenses, PII handling, abuse/hallucination controls, and audit trail.

- Contribute to internal AI tooling : SDKs, templates, and reusable components to accelerate future AI usecase.

Must have :

- Strong software engineering in Python (and one of Node/Java/Go), REST/gRPC APIs, queues, and microservices on cloud infra.

- Hands on experience shipping at least one AI powered product to production (e.g., search, recommendations, chatbots, summarization, classification)

- Practical knowledge of LLM concepts: prompts, context engineering, embeddings, vector search, basic evaluation metrics, and latency/cost tradeoffs.

- Solid understanding of integration patterns with third-party AI providers (OpenAI, Anthropic, etc.) and vector DB

- Hand-on & good understanding of at least one agentic framework like Langgraph.