Python + AI Lead
Stellent IT LLC
Dallas, TX, United States
26 days ago
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Role details
Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
5 years minimum
Working hours
Regular working hours
Job source
Tech stack
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
+15 more
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
Requirements
- 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).
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Prepare application
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