Overview
Hybrid
Depends on Experience
Accepts corp to corp applications
Contract - W2
Contract - 12 Month(s)
Skills
Agentic frameworks
Langgraph
CrewAI
OpenAI APIs
MLOps
Vertex AI
Job Details
Must be local to TX
Description:
- Strategic AI Leadership: Translate complex business challenges into clear, actionable AI/ML strategies and comprehensive technical roadmaps, ensuring alignment with organizational goals.
- Team Leadership & Mentorship: Guide, mentor, and develop a high-performing team of data scientists and machine learning engineers, fostering a collaborative culture of innovation, continuous learning, and technical excellence.
- End-to-End Solution Delivery: Oversee the entire machine learning lifecycle, from problem definition and data exploration to model design, training, validation, deployment, monitoring, and ongoing optimization.
- Production Deployment: Lead the successful deployment of robust, scalable, and high-impact ML solutions into production environments, ensuring they generate measurable and significant business value.
- Technical Excellence & Best Practices: Champion MLOps best practices, ensuring robust model governance, versioning, testing, and monitoring for all AI solutions.
- Technology Evaluation & Integration: Actively research, evaluate, and integrate new AI technologies, frameworks (including Agentic frameworks), tools, and cutting-edge research findings to maintain a competitive edge and drive innovation.
- Stakeholder Collaboration: Collaborate effectively with cross-functional teams, including engineering, product, and business units, to understand requirements, manage expectations, and ensure successful project delivery.
- Ethical AI Development: Apply a strong understanding of ethical AI principles, fairness, transparency, and data privacy throughout the design, development, and deployment of all AI solutions.
Qualifications:
Experience: overall 12+ years of experience and min 8+ years of progressive experience in data science roles, with a significant focus on leading AI/ML initiatives.
Technical Proficiency:
Demonstrated proficiency in Agentic frameworks (e.g., Langgraph, CrewAI), Python, and SQL.
Deep expertise in the end-to-end ML lifecycle, including model design, training, validation, deployment, and monitoring.
Proven experience deploying scalable ML solutions in production environments.
Proficiency in major cloud platforms (e.g., AWS, Azure, Google Cloud Platform) for scalable AI solution development and deployment, including relevant services (e.g., SageMaker, Azure ML, Vertex AI).
Experience with Machine Learning Frameworks such as TensorFlow, PyTorch, and Scikit-learn.
Familiarity with data processing and manipulation libraries/tools like Pandas and Apache Spark.
Understanding of MLOps tools and practices (e.g., MLflow, Kubeflow, Docker, Kubernetes) for model lifecycle management.
Proficiency with version control systems, particularly Git.
Experience with both SQL and NoSQL databases.
Familiarity with AI/ML specific tools and platforms such as OpenAI APIs, and integration platforms like WSO2 for AI service orchestration.
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