Gen AI Lead - Dallas, TX ( Hybrid)

Location: Dallas, TX (Hybrid)W2 Contract

Role Overview: Sr AI/ML Lead with around 15+ years of hands-on experience in developing and implementing Machine Learning and Generative AI solutions. The role involves designing, developing, and deploying end-to-end AI/ML applications using Python, popular ML frameworks, and modern web technologies.

Technical Skills: Must Have Skills Machine learning development lifecycle - (Data preparation, Data visualization, Statistical Analysis, feature engineering, Predictive modeling, Model deployment, Model monitoring), CI/CD, MLOps, GenerativeAI, Causal Inference, Time series analysis, Forecasting, Anomaly detection, Hypothesis testing, A/B testing, Git Actions, Tableau, Power BI, Thought Spot, Web Scraping Data & Engineering – SQL, MySQL, Postgres, Spark, S3, Trino, Data Factory, ETL, Data pipelines, Databricks and distributed computing. Programming Languages: SQL, Pyspark, Scala, R, Python, SASGen AI & Agents – Prompt Engineering, RAG, Vector DB, Agentic Frameworks, MCP, Large Language Models (LLMs), LangChain, LangGraph, Explainable AI, Conversational AI, Chat bots and Tuning, LLM Evaluations and Cost monitoring, Hugging Face

Tools/Framework: Git, TensorFlow, PyTorch, PySpark, AWS, MLflow, Docker, Kubernetes, Databricks, SparkSQL, OpenCV, Azure, YOLO, Scikit-Learn, FastAPI, Flask, Django, Keras, Pandas, NumPy, Polars, SciPy, Matplotlib, Seaborn, Plotly, Streamlit Cloud & MLOps: AWS Sagemaker, Azure ML, or GCP AI Platform; Git, Docker, CI/CD.Role Activities: Design, develop, and deploy AI/ML and Generative AI models for enterprise and telecom use cases. Build and optimize data pipelines for training, validation, and inference processes. Develop web-based AI applications using frameworks like Flask, FastAPI, or Django. Implement LLM-based solutions such as chatbots, summarization, and RAG-based systems. Collaborate with data scientists, solution architects, and business teams to understand functional requirements and translate them into technical implementations. Participate in proof-of-concept (PoC) development for AI/ML and automation use cases. Conduct model evaluation, fine-tuning, and performance optimization. Work with APIs, data sources, and cloud-based ML services (AWS, Azure, GCP).Follow best practices in MLOps, model versioning, and CI/CD integration. Prepare technical documentation, training materials, and demo presentations.

Preferred Qualifications: Certification in AI/ML, Deep Learning, or Generative AI is a plus.