Python AI Developer
Tekwissen
Overview:
TekWissen is a global workforce management provider headquartered in Ann Arbor, Michigan that offers strategic talent solutions to our clients world-wide. Our client provider of digital technology and transformation, information technology and services
Position: Python AI Developer Location:Seattle,WA 98008 Duration: 6 Months Job Type: Temporary Assignment Work Type: Onsite Job Description: Role Summary:
TekWissen is a global workforce management provider headquartered in Ann Arbor, Michigan that offers strategic talent solutions to our clients world-wide. Our client provider of digital technology and transformation, information technology and services
Position: Python AI Developer Location:Seattle,WA 98008 Duration: 6 Months Job Type: Temporary Assignment Work Type: Onsite Job Description: Role Summary:
- 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.
- 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
- 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)
- 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
- 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)
Vacancy posted 14 hours ago
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