Senior Software Engineer - Python

<ul>
<li>Design, build, and scale backend services and APIs using Python with FastAPI, Django, and Flask</li>
<li>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</li>
<li>Develop RAG pipelines, including document ingestion, chunking, embeddings, vector databases, retrieval tuning, and evaluation</li>
<li>Integrate and optimize LLMs (OpenAI, Anthropic Claude, Gemini, open-source models) for accuracy, latency, and cost</li>
<li>Build data pipelines, analytics, and ML workflows using Pandas, NumPy, scikit-learn, and PyTorch or TensorFlow</li>
<li>Set up LLM evaluation, guardrails, monitoring, and observability (LangSmith, Langfuse, or similar) for production AI systems</li>
<li>Deploy and operate services on AWS, GCP, or Azure with Docker, Kubernetes, and CI/CD pipelines</li>
<li>Lead technical design, break down work, mentor engineers, and set code-quality and AI-engineering standards</li>
<li>Present AI solutions, demos, and technical roadmaps to clients, stakeholders, and leadership</li>
<li>Take part in on-call rotations and production incident response</li>
</ul><h2>Requirements</h2><ul>
<li>3 to 6 years of professional software engineering experience, mostly in Python</li>
<li>Strong production experience with FastAPI plus Django and/or Flask, including async programming and REST API design</li>
<li>Hands-on experience building LLM-powered applications, including at least one RAG or agent-based system in production</li>
<li>Working knowledge of vector databases (Pinecone, Weaviate, Qdrant, pgvector) and embedding models</li>
<li>Solid data science fundamentals: data wrangling, statistics, model evaluation, and working with structured and unstructured data</li>
<li>Strong SQL skills (PostgreSQL) and experience with NoSQL stores and Redis</li>
<li>Hands-on cloud experience (AWS, GCP, or Azure) and containerization with Docker</li>
<li>Strong testing habits (pytest) and clean, typed Python (type hints, Pydantic)</li>
<li>Understanding of API security, OAuth 2.0, JWT, and responsible handling of sensitive data</li>
<li>Excellent communication skills, with the ability to explain complex AI concepts simply, run client demos, and confidently represent and sell our technical approach</li>
<li>Proven ability to lead projects and guide team members from proof of concept to production</li>
<li>Curiosity and the ability to evaluate and adopt new AI tools and models quickly<br><br>Nice to have</li>
<ul>
<li>Experience with fine-tuning, prompt optimization, or model distillation</li>
<li>MLOps tools (MLflow, Kubeflow, SageMaker, Vertex AI, Bedrock)</li>
<li>Experience building MCP servers or multi-agent systems</li>
<li>Big data or streaming tools (Spark, Airflow, Kafka, dbt)</li>
<li>Familiarity with Node.js or TypeScript for full-stack collaboration</li>
<li>Published projects, research, Kaggle work, or open-source contributions in AI/ML</li>
<li>AWS, GCP, or Azure AI or ML certifications</li>
</ul>
</ul>