Role: Python (AI/ML)

Location: McLean, VA (Onsite)

Interview: F2F Interview

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

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 GCP (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