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.
Technical Requirements: Python programming (strong fundamentals, OOP, writing APIs, debugging).
Hands-on experience building GenAI/LLM solutions: RAG / embeddings / vector DB / prompt engineering.
Experience with FastAPI or Flask (building and serving APIs).
Understanding of LLM application lifecycle (prompting, evaluation, versioning, deployment basics).
Knowledge of at least one cloud platform: AWS or Azure.
Basic understanding of Git, code reviews, and deployment workflows.
Additional Responsibilities: 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.
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