We are looking for a Python AI Engineer (2–3 yrs) who can build and productionize AI/GenAI solutions—especially LLM-powered applications such as RAG systems, summarization, classification, and agentic workflows. The role is engineering-led: strong Python coding, API development, deployment readiness, and basic operational practices (monitoring/evaluation/guardrails). This is not an ML platform role. ✅ Key Responsibilities GenAI / LLM Engineering Build LLM-powered applications (chatbots, copilots, summarization, knowledge assistants) using OpenAI/Azure OpenAI/Anthropic/Gemini or open-source LLMs. Implement RAG pipelines: data ingestion → chunking → embeddings → vector search → prompt assembly → response generation. Improve response quality using prompt engineering, retrieval tuning (hybrid search, metadata filters), and basic RAG evaluation practices. ML Engineering (non-platform) Develop and deploy ML components (classification, NLP, forecasting) using scikit-learn / PyTorch / TensorFlow as needed. Package AI/LLM solutions into production-grade services using FastAPI/Flask. Write clean, reusable Python modules and follow engineering best practices (testing, logging, code quality). Deployment & Operations (LLMOps exposure) Support deployment to cloud environments: AWS (SageMaker/ECS/Lambda) or Azure (Azure ML/AKS/App Services). Implement basic observability: logs, error handling, latency tracking, token usage tracking (where applicable). Assist in quality, safety, and governance practices: PII redaction, content filtering, prompt-injection mitigation, secure access controls. Vector databases: Pinecone / Qdrant / Chroma / Weaviate / FAISS. Frameworks: LangChain / LangGraph / LlamaIndex / Semantic Kernel. Evaluation tools: RAGAS / TruLens / DeepEval, prompt testing frameworks. Containerization: Docker (Kubernetes is optional). CI/CD exposure: GitHub Actions / Azure DevOps / Jenkins. Data pipelines: Airflow / Prefect / Databricks. Safety tooling: Presidio, content safety filters, access control patterns.