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# Python AI Developer

- **Company:** Sumeru INC
- **Location:** Bellevue, WA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Clean Code Principles, Java (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, Software Applications, Microsoft Azure, Code Review, Continuous Integration, Cursor (Graphical User Interface Elements), Database Design, Software Debugging, Programming Tools, Django Web Framework, Python (Programming Language), PostgreSQL, Machine Learning, MongoDB, MySQL, NoSQL, Performance Tuning, Scrum Methodology, Systems Development Life Cycle, Queueing Systems, RabbitMQ, Redis, Software Tools, Tensorflow, Azure Machine Learning, SQL Databases, Data Streaming, Systems Integration, Web Application Frameworks, Datadog, Feature Engineering, Chatbots, GitHub Copilot, Pytorch, Retrieval-Augmented Generation, Flask (Web Framework), Large Language Models, Grafana, Multi-Agent Systems, Prompt Engineering, Apache Spark, Core Api, Git, Fastapi, Build Management, Pytest, Containerization, Integration Tests, Kubernetes, HuggingFace, Apache Kafka, Machine Learning Operations, Celery, Restful APIs, Amazon Simple Queue Service (SQS), Splunk, Code Restructuring, Data Pipelines, Docker, Microservices
- **Published:** September 12, 2026
- **Apply:** https://www.careerjet.com/jobad/use41ade4315812b1a27ea0d7e3c3d184b

## About the Role

3 6 years of professional Python development experience
Strong proficiency in Python 3.x and at least one web framework (FastAPI, Flask, or Django)
Hands-on experience building and integrating LLM/AI applications (chatbots, RAG, summarization, classification, or agentic systems)
Experience with at least one ML/AI framework: LangChain, LlamaIndex, Hugging Face, PyTorch, or TensorFlow
Experience with RESTful API design and microservices architecture
Solid understanding of SQL and database design
Practical, hands-on experience using AI coding assistants (Copilot, Claude Code, Cursor, etc.) in a real development workflow
Familiarity with unit/integration testing frameworks (pytest, unittest)
Working knowledge of Git, CI/CD pipelines, and containerization (Docker)
Preferred Qualifications
Experience with vector databases and embedding models
Familiarity with prompt engineering, LLM evaluation, and guardrails/safety practices
Exposure to cloud platforms (AWS, Azure, or GCP) and their AI/ML services (SageMaker, Vertex AI, Azure ML)
Experience with Kafka or similar event-streaming platforms
Understanding of MCP (Model Context Protocol) or agentic tool-calling frameworks
Experience with MLOps tooling (MLflow, Weights & Biases, Kubeflow)
Prior experience in Agile/Scrum environments
Nice to Have
Contributions to internal AI tooling adoption or engineering productivity initiatives
Experience with observability tools (Datadog, Splunk, Grafana)
Familiarity with data engineering tools (Airflow, Spark, dbt)

## Description

We're looking for a mid-level Python Developer with hands-on AI/ML experience to design, build, and deploy AI-powered applications and services. You'll work across the stack - from data pipelines and model integration to production APIs - while also using AI coding assistants as a core part of your daily engineering workflow.
Key Responsibilities
Core Development
Design, build, and maintain Python services and APIs (FastAPI, Flask, or Django)
Write clean, well-tested, maintainable code following established engineering standards
Participate in code reviews, design discussions, and sprint planning
Debug and resolve production issues, including performance tuning and root cause analysis
Work with relational and/or NoSQL databases (PostgreSQL, MySQL, MongoDB, Redis)
Build and consume REST/gRPC APIs; work with message queues (Kafka, RabbitMQ, SQS) and async task frameworks (Celery)
AI/ML Engineering
Build and deploy ML/LLM-based services using Python, integrating models via APIs (OpenAI, Anthropic) or self-hosted inference
Design and maintain RAG (Retrieval-Augmented Generation) pipelines using vector databases (pgvector, Pinecone, Weaviate, FAISS)
Develop agentic workflows and tool-calling integrations connecting LLMs to internal APIs and data sources
Use frameworks such as LangChain, LlamaIndex, Hugging Face Transformers, or PyTorch for model development and orchestration
Fine-tune, evaluate, and monitor model performance; implement prompt engineering and evaluation harnesses
Collaborate with Data Science/ML teams on feature engineering, model serving infrastructure, and MLOps practices
Apply sound judgment on when/where AI capabilities add real product value vs. added complexity
AI-Assisted Engineering Practices
Use AI coding assistants (GitHub Copilot, Claude Code, Cursor, or similar) to accelerate development, refactoring, and debugging
Apply AI-assisted test generation and code review practices to improve velocity without sacrificing quality
Continuously evaluate and adopt emerging AI-augmented engineering tools and workflows
Mentor peers on effective, responsible use of AI tools in the SDLC, Here is what you will do and learn to be successful in this role: Drive product strategy: Define and own the product strategy for Python and Java developer tools, language-specific…

 + 18 days ago


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