Why This Role Stands Out
This hybrid role offers a fantastic opportunity to lead AI/ML initiatives, leveraging your extensive Python and AI experience to drive innovation. You'll thrive here if you're passionate about bringing cutting-edge AI systems to production and enjoy a collaborative environment. Apply today to shape the future of AI at Pristine Resource!
Quick Overview
Seniority
Mid Senior
Work mode
Hybrid
Location
Dallas, TX, United States
Posted
1 week ago
DockerFastAPIFlaskAWSMLOpsMLflowAirflowAzureGitGoogle CloudKafkaKubernetesLLMPython
Job Description
Position: Python + AI Lead
Location: Dallas, TX (Hybrid)
Hire Type: W2 Only
Minimum Qualifications
- · 10+ Years of overall exp and 5+ 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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