Gen AI Engineer_5+ years

Job Summary

We are looking for a skilled Generative AI Engineer with hands-on experience in developing and deploying LLM-powered applications, Retrieval-Augmented Generation (RAG) solutions, and AI agents. The ideal candidate should have strong Python programming skills and experience with modern GenAI frameworks, cloud-based AI services, and production-grade AI applications.

Key Responsibilities

Design, develop, and deploy Generative AI and LLM-based applications.

Build RAG pipelines using vector databases, embeddings, and semantic search.

Develop AI agents and multi-agent workflows using LangChain, LangGraph, or similar frameworks.

Integrate LLMs with enterprise applications, APIs, and external data sources.

Implement prompt engineering, evaluation, guardrails, and hallucination mitigation.

Develop scalable backend services using Python, FastAPI, or Flask.

Optimize model performance, latency, token usage, and inference costs.

Deploy and monitor AI applications on AWS, Azure, or GCP.

Collaborate with data engineering, cloud, and product teams to deliver production-ready solutions.

Mandatory Skills

Strong Python programming and API development.

Hands-on experience with Generative AI, LLMs, and prompt engineering.

Practical experience building RAG applications.

Experience with LangChain, LangGraph, LlamaIndex, or equivalent frameworks.

Understanding of embeddings, vector databases, semantic search, and chunking strategies.

Experience integrating LLMs with enterprise systems and REST APIs.

Knowledge of LLM evaluation, responsible AI, and production deployment.

Good to Have

Experience with AWS Bedrock, Azure OpenAI, or Google Vertex AI.

Knowledge of agentic AI, multi-agent orchestration, and Model Context Protocol (MCP).

Experience with vector databases such as Pinecone, FAISS, Weaviate, or Chroma.

Familiarity with Docker, Kubernetes, CI/CD, and cloud monitoring.

Experience with GenAI observability, tracing, logging, and cost optimization.