AI System Engineer

Python Fundamentals (Must Have)

• Deep expertise in Python 3.10+, including asyncio, multithreading/multiprocessing, decorators, generators, and metaclasses
• Proficiency with foundational packages: NumPy, Pandas, Pydantic, httpx/requests, dataclasses, typing
• Strong grasp of clean code principles, SOLID design, and Pythonic idioms
• Experience writing unit/integration tests with pytest and maintaining high code coverage
• Familiarity with linting/formatting toolchains (ruff, black, isort, mypy) and pre-commit hooks
• Experience with dependency and environment management (Poetry, uv, pip, venv, conda)

Agentic AI, LangChain & MCP (Core Focus)

• Proven hands-on experience with Model Context Protocol (MCP) designing, building, and maintaining MCP servers and clients
• Strong working experience with FastMCP for building Python-based MCP servers with tools, resources, and prompts
• Expert-level experience with LangChain (chains, agents, memory, retrievers, output parsers, LCEL)
• Experience with LangGraph for stateful, multi-agent, and graph-based agentic workflows
• Understanding of tool/function calling, structured outputs, and agent-to-agent communication patterns
• Experience integrating multiple LLM providers (Anthropic Claude, OpenAI, Azure OpenAI, Gemini, open-source models)
• Knowledge of RAG architecture: chunking strategies, embeddings, hybrid search, re-ranking, and evaluation

Backend & API Development

• 5+ years building production APIs with FastAPI, Flask, or Django REST Framework
• Experience with streaming responses (SSE/WebSockets) for real-time LLM output
• Solid understanding of authentication/authorization mechanisms (OAuth2, JWT, API key management)
• Experience designing scalable microservices and event-driven architectures (Kafka, RabbitMQ, Celery)

Data & Storage

• Strong SQL skills (PostgreSQL, MySQL) and experience with ORMs (SQLAlchemy)
• Hands-on experience with vector databases: Chroma, Pinecone, Qdrant, Weaviate, pgvector, or FAISS
• Experience with caching layers (Redis) and NoSQL stores (MongoDB, DynamoDB)
• Data preprocessing, ETL pipeline development, and working with structured/unstructured data

ML/AI Foundations

• Working knowledge of machine learning fundamentals: embeddings, similarity metrics, classification, evaluation
• Familiarity with PyTorch, TensorFlow, or scikit-learn for model training/inference where needed
• Experience with Hugging Face ecosystem (Transformers, datasets, model hub)
• Understanding of prompt engineering, few-shot learning, and LLM evaluation frameworks (RAGAS, DeepEval, LangSmith evals)

Cloud, DevOps & MLOps

• 4+ years deploying applications on AWS, Azure, or Google Cloud Platform (Lambda, ECS/EKS, Cloud Run, Azure Functions)
• Proficiency with Docker; working knowledge of Kubernetes and Helm
• CI/CD experience with GitHub Actions, GitLab CI, or Azure DevOps
• Experience with LLM observability and tracing tools (LangSmith, Langfuse, Arize Phoenix, OpenTelemetry)
• Familiarity with secrets management, rate limiting, and cost monitoring for LLM workloads

Security & Responsible AI

• Experience implementing guardrails, input/output validation, and PII handling in AI pipelines
• Awareness of prompt injection risks and mitigation strategies in agentic/MCP-based systems
• Understanding of compliance considerations (SOC 2, GDPR, HIPAA) when handling sensitive data

Collaboration & Leadership

• Experience mentoring engineers, conducting code reviews, and setting technical standards
• Ability to translate business problems into AI solution architectures
• Excellent communication skills with both technical and non-technical stakeholders
• Comfortable in Agile/Scrum delivery models with tools like Jira and Confluence

Nice to Have

• Contributions to open-source AI/LLM projects (LangChain, MCP servers, etc.)
• Experience with fine-tuning (LoRA/QLoRA) or self-hosted model serving (vLLM, Ollama, TGI)
• Knowledge of A2A protocols, CrewAI, AutoGen, or other multi-agent frameworks
• Experience building Slack/Teams bots or IDE integrations powered by MCP

Education & Experience

• Minimum 7-10 years of overall software engineering experience with strong Python expertise
• 3+ years of hands-on experience building LLM-powered or AI/ML applications in production
• Bachelor's/Master's degree in Computer Science, Engineering, AI/ML, or equivalent industry experience
• Demonstrated experience owning end-to-end delivery of AI products from design to deployment