Python AI Developer

Apptad Inc.
Seattle, WA, United States
about 2 months ago
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Role details

Contract type
Temporary to permanent
Employment type
Full-time (> 32 hours)
Experience level
Experienced
Working hours
Regular working hours

Tech stack

Artificial Intelligence Airflow Amazon Web Services Microsoft Azure Continuous Integration Cursor Database Design Django Web Framework Python (Programming Language) Software Tools Tensorflow Azure Machine Learning
+26 more
SQL Databases Data Streaming Systems Integration Web Application Frameworks Datadog Chatbots Pytorch Flask (Web Framework) Large Language Models Grafana Multi-Agent Systems Prompt Engineering Apache Spark Git Fastapi Pytest Containerization Integration Tests Kubernetes HuggingFace Apache Kafka Machine Learning Operations Restful APIs Splunk Docker Microservices

Requirements

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)

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