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# Python + AI Lead

- **Company:** Stellent IT LLC
- **Location:** Dallas, TX, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, Microsoft Azure, Cloud Computing, Information Engineering, DevOps, Python (Programming Language), Software Engineering, Data Streaming, Systems Integration, Google Cloud, Large Language Models, Multi-Agent Systems, Prompt Engineering, Apache Spark, Generative AI, Backend, Git, Containerization, Kubernetes, Infrastructure Automation Frameworks, Apache Kafka, Machine Learning Operations, Restful APIs, Docker
- **Published:** September 11, 2026
- **Apply:** https://www.dice.com/job-detail/02905e37-3d35-4be2-ad4b-d0e8ed0a1b1b

## About the Role

* Overall Experience: 10+ years of total software engineering experience, including 5+ years of dedicated, hands-on Python development.
 * Production AI Delivery: Proven track record of delivering at least one production-grade AI/ML or LLM-powered system through its complete lifecycle.
 * AIDLC Proficiency: Practical understanding of the AI Development Life Cycle (data engineering, experimentation, evaluation, deployment, and monitoring).
 * LLM Application Stack: Deep experience in prompt engineering, Retrieval-Augmented Generation (RAG), and integrating major LLM model provider APIs.
 * Backend & Cloud: Strong foundation in designing/consuming RESTful APIs, containerization with Docker, and deploying backend systems on AWS, Google Cloud Platform, or Azure.
 * DevOps Foundation: Solid grounding in Git version control and modern CI/CD release workflows.
 * Cross-Functional Communication: Clear technical communication skills with a track record of collaborating across engineering, data science, and product teams., * Agentic Frameworks: Experience with LangChain, LangGraph, LlamaIndex, or multi-agent orchestration frameworks.
 * Vector Databases: Hands-on work with Pinecone, Weaviate, pgvector, FAISS, and embedding retrieval strategies.
 * MLOps / LLMOps: Exposure to MLflow, Weights & Biases, model registries, feature stores, and safety guardrails.
 * Infrastructure & Data: Familiarity with Kubernetes, Infrastructure-as-Code (IaC), and streaming pipelines (Kafka, Spark, Airflow).