Highlights
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. Overview: TekWissen is a global workforce management provider headquartered in Ann Arbor, Michigan that offers strategic talent solutions to our clients world-wide.
Numbers & Facts
| Location | Bellevue, WA |
| Industry | Computer/IT Services |
| Salary | $46.32 |
| Company Size | 100 to 499 employees |
| Year Founded | 2009 |
| Website | http://www.tekwissen.com/ |
Description
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:
- 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
Required Qualifications:
- 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)
TekWissen Group is an equal opportunity employer supporting workforce diversity.
About Company
WE THE TEKWISSEN PEOPLE
TekWissen offers you a broader portfolio of services, industry-leading solutions, and the meaningful innovations that give you greater flexibility and speed to respond to market dynamics, reduced costs and risk to improve enterprise performance, and increased productivity to enable growth.
To keep pace with global market demands, TekWissen keeps its finger on the pulse of change. Our organized approach to guiding a project from its inception to closure. Managing projects is becoming more and more important as we enter the digital era. To cope with the pace that this transition demands, a method is required to manage projects so they can yield quality work, while incorporating efficient use of time and resources.
Project involves identifying which quality standards are relevant to the project and determining how to satisfy them.
It is important to perform quality planning during the Planning Process and should be done alongside the other project planning processes because changes in the quality will likely require changes in the other planning processes, or the desired product quality may require a detailed risk analysis of an identified problem. It is important to remember that quality should be planned, designed, then built in, not added on after the fact.
Capabilities and accomplishments in one TekWissen business enhance the opportunity for success in the others. Put simply, TekWissen's unique combination of attributes promotes success.Similar Job Searches
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