Why This Role Stands Out
This role offers a fantastic opportunity to architect and develop cutting-edge AI-powered systems, leveraging your Python and LLM expertise. You'll thrive here if you're a seasoned developer with a passion for innovation and a desire to shape the future of AI solutions, with a hybrid work model offering flexibility.
Quick Overview
Seniority
Mid Senior
Work mode
On Site
Location
TX, United States
Posted
6 days ago
DockerFastAPIFlaskAWSMLOpsMLflowAirflowAzureGitGoogle CloudKafkaKubernetesLLMPython
Job Description
Python Architect / Developer with AI exp
Southlake, TX (4 days work from office)
3 months CTH or FTE
Minimum Qualifications
- 10+ years of professional software development experience, with strong, current Python expertise.
- Demonstrable experience delivering at least one AI/ML or LLM-powered system to production and supporting it through its life cycle.
- Practical familiarity with the AI Development Life Cycle (AIDLC): data handling, experimentation, evaluation, deployment, and monitoring.
- Hands-on experience with LLM application development - prompt engineering, RAG, and integrating APIs such as those from major model providers.
- Proficiency designing and consuming RESTful APIs and building scalable backend services.
- Working knowledge with cloud platforms (AWS, Google Cloud Platform, or Azure) and containerization (Docker).
- Solid grounding with version control (Git) and CI/CD workflows.
- Strong communication skills and the ability to work across engineering, data, and product teams.
Preferred Qualifications
- Experience with agentic frameworks and orchestration (e.g., LangChain, LangGraph, LlamaIndex, or multi-agent frameworks).
- Familiarity with vector databases (e.g., Pinecone, Weaviate, pgvector, FAISS) and embedding-based retrieval.
- Exposure to MLOps/LLMOps tooling - MLflow, Weights & Biases, model registries, and feature stores.
- Experience with Kubernetes and infrastructure-as-code.
- Understanding of model evaluation, responsible AI, safety guardrails, and observability for LLM systems.
- Background with data pipelines and streaming (e.g., Kafka, Spark, or Airflow).
Core Tech Stack
Python FastAPI / Flask LLM & Agentic frameworks Vector databases Docker CI/CD Cloud (AWS / Google Cloud Platform / Azure) MLOps/LLMOps tooling
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