Generative AI Engineer

AI Engineer (production / apps focus):

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.

 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.

 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 use case.

Skills required:

AI Engineer role demands more than AI-based augmentation with in-depth understanding of

concepts like-

-RAG

-GenAI, LLM finetuning, Prompt engineering

-Multi-Agent framework (langchain, langraph etc with hands on experience)

-Eval generation and their importance

-Tokens usage and optimisations.

-Model/mcp gateway

Ideal profile:

 Strong software engineering in Python (and one of Node/Java/Go),

REST/gRPC APIs, queues, and microservices on cloud infra.

 Handson 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 trade-offs.

 Solid understanding of integration patterns with third party AI providers (OpenAI,

Anthropic, etc.) and vector DB

 Hand-on & good understanding of atleast one agentic framework like Langgraph.