Senior AI Engineer (Python, LLMs & RAG) Job - Apply Now

We are looking for a Senior AI Engineer to design, build, and scale Python backend services and agentic AI systems. You will build RAG pipelines, integrate and optimize LLMs, and run production AI systems with proper evaluation and monitoring. You will also lead technical design, mentor engineers, and present AI solutions to clients and stakeholders.

Key Responsibilities

• Design, build, and scale backend services and APIs in Python using FastAPI, Django, and Flask.
• Build and ship agentic AI systems, including multi-step agents, tool calling, MCP integrations, and orchestration with LangChain/LangGraph, LlamaIndex, CrewAI, or the Claude Agent SDK.
• Develop RAG pipelines covering document ingestion, chunking, embeddings, vector databases, retrieval tuning, and evaluation.
• Integrate and optimize LLMs (OpenAI, Anthropic Claude, Gemini, open-source models) for accuracy, latency, and cost.
• Build data pipelines, analytics, and ML workflows using Pandas, NumPy, scikit-learn, and PyTorch or TensorFlow.
• Set up LLM evaluation, guardrails, monitoring, and observability (LangSmith, Langfuse, or similar) for production AI systems.
• Deploy and operate services on AWS, GCP, or Azure with Docker, Kubernetes, and CI/CD pipelines.
• Lead technical design, break down work, mentor engineers, and set code-quality and AI-engineering standards.
• Present AI solutions, demos, and technical roadmaps to clients, stakeholders, and leadership.
• Take part in on-call rotations and production incident response.

Requirements

• 3 to 6 years of professional software engineering experience, mostly in Python.
• Strong production experience with FastAPI plus Django and/or Flask, including async programming and REST API design.
• Hands-on experience building LLM-powered applications, including at least one RAG or agent-based system in production.
• Working knowledge of vector databases (Pinecone, Weaviate, Qdrant, pgvector) and embedding models.
• Solid data science fundamentals: data wrangling, statistics, model evaluation, and working with structured and unstructured data.
• Strong SQL skills (PostgreSQL) and experience with NoSQL stores and Redis.
• Hands-on cloud experience (AWS, GCP, or Azure) and containerization with Docker.
• Strong testing habits (pytest) and clean, typed Python (type hints, Pydantic).
• Understanding of API security, OAuth 2.0, JWT, and responsible handling of sensitive data.
• Proven ability to lead projects and guide team members from proof of concept to production.

Skills

Technical Skills

• Python, FastAPI, Django, and Flask
• Agentic AI, MCP, and orchestration frameworks (LangChain/LangGraph, LlamaIndex, CrewAI, Claude Agent SDK)
• RAG pipelines and vector databases
• LLM integration, evaluation, guardrails, and observability
• Pandas, NumPy, scikit-learn, and PyTorch or TensorFlow
• PostgreSQL, NoSQL, and Redis
• AWS, GCP, or Azure with Docker, Kubernetes, and CI/CD
• pytest, type hints, and Pydantic
• API security (OAuth 2.0, JWT)

Soft Skills

• Excellent communication, explaining complex AI concepts simply
• Running client demos and representing the technical approach confidently
• Leading projects and mentoring engineers
• Curiosity and quickly adopting new AI tools and models

Nice to Have

• Fine-tuning, prompt optimization, or model distillation.
• MLOps tools (MLflow, Kubeflow, SageMaker, Vertex AI, Bedrock).
• Experience building MCP servers or multi-agent systems.
• Big data or streaming tools (Spark, Airflow, Kafka, dbt).
• Familiarity with Node.js or TypeScript for full-stack collaboration.
• Published projects, research, Kaggle work, or open-source contributions in AI/ML.
• AWS, GCP, or Azure AI or ML certifications.