Demonstrated experience leading AI Engineering, Machine Learning Engineering, Data Science, or applied research initiatives from concept through production deployment.
Expert-level hands-on programming skills in Python and strong experience building production-quality software, reusable frameworks, and scalable AI systems.
Deep understanding of machine learning fundamentals and practical application of supervised learning, ensemble methods, feature engineering, model evaluation, experimentation, and production model risk controls.
Proven track record as a hands-on practitioner developing, deploying, debugging, and improving machine learning solutions in production environments with measurable business impact.
Strong hands-on experience writing, reviewing, testing, and maintaining production-grade code for AI/ML applications.
Experience working with LLMs, prompt engineering, reasoning and planning frameworks, RAG architectures, vector databases, AI agents, and Generative AI application development.
Experience with distributed computing platforms such as Spark, Databricks, or similar big data technologies.
Familiarity with cloud platforms such as Azure, AWS, or GCP and associated AI/ML services.
Strong understanding of MLOps principles, including model deployment, model monitoring, CI/CD, ML lifecycle management, governance, observability, and operational reliability.
Experience working with SQL and large-scale structured and unstructured datasets.
Strong analytical thinking and problem-solving abilities with a passion for solving complex real-world challenges.
Excellent communication, technical leadership, and collaboration skills with the ability to influence architecture decisions and work effectively across cross-functional teams.
Ability to mentor engineers, raise engineering standards, and bring strong learning agility, intellectual curiosity, and thought leadership in AI and machine learning.