Machine Learning Engineer

• 6-month Contract to Hire as a W2 employee of Talent Groups. We are unable to offer sponsorship for this role. • Onsite, 4 days a week in Irving, TX 75038. Our Irving-based client is seeking a Full-Stack AI/ML Engineer to design, build, and deploy AI-driven solutions that transform data into production-ready intelligence. This role spans the full AI lifecycle — from data acquisition and model development to LLM integration, deployment, and continuous optimization. The engineer will collaborate closely with product owners, program managers, and cross-functional Scrum teams to deliver scalable, high-impact AI capabilities aligned with business goals. Key Responsibilities • Design and implement AI/ML solutions that automate, optimize, and enhance business workflows. • Acquire and preprocess structured and unstructured data from APIs, databases, OCR pipelines, and document sources. • Conduct exploratory data analysis (EDA) and build predictive and statistical models using Python and modern ML frameworks. • Develop and fine-tune Large Language Model (LLM) pipelines using tools such as OpenAI, Azure OpenAI, Hugging Face, and LangChain. • Implement retrieval-augmented generation (RAG) and document intelligence systems. • Develop and deploy production-grade APIs and microservices (FastAPI or similar) integrated with MLOps best practices. • Collaborate with data engineers on pipeline efficiency and software engineers on product integration. • Monitor, retrain, and optimize deployed models for performance and accuracy. • Research and prototype emerging AI techniques, including multimodal models and AI agents. • Document architectures, design decisions, and experiment outcomes for transparency and reproducibility. • Contribute as an active member of an Agile/Scrum team through sprint planning, backlog grooming, and daily stand-ups. • Participate in code reviews, maintain clean coding standards, and contribute to shared AI libraries and internal frameworks. Qualifications • 5+ years of hands-on experience in data science, ML engineering, or applied AI with production deployments. • Strong proficiency in Python (Pandas, NumPy, scikit-learn, LangChain, LangGraph, etc.) and SQL. • Experience with ML frameworks such as TensorFlow or PyTorch. • Skilled in data acquisition, ETL pipelines, and feature engineering across APIs, cloud storage, and databases. • Proficiency in FastAPI for model deployment as microservices. • Experience with MLOps tools and practices (Docker, Kubernetes, CI/CD). • Hands-on experience with cloud platforms (Azure, AWS, or GCP) and their AI/ML ecosystems. • Working knowledge of LLMs and Generative AI frameworks. • Strong understanding of EDA, model validation, and experiment tracking. • Familiarity with vector databases for semantic search and RAG workflows. • Comfortable in Agile/Scrum environments with strong communication and documentation skills. • Proven ability to take AI/ML models from prototype to production and drive continuous improvement.