About the role
- Design, build, and scale backend services and APIs using Python with FastAPI, Django, and Flask - Build and ship agentic AI systems: multi-step agents, tool calling, MCP integrations, and orchestration with frameworks like LangChain/LangGraph, LlamaIndex, CrewAI, or the Claude Agent SDK - Develop RAG pipelines, including 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 - Excellent communication skills, with the ability to explain complex AI concepts simply, run client demos, and confidently represent and sell our technical approach - Proven ability to lead projects and guide team members from proof of concept to production - Curiosity and the ability to evaluate and adopt new AI tools and models quickly
Nice to have
- Experience with 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
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