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

Senior Software Engineer - Python

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