ML Engineering

• AI Program Leadership: Lead the design, execution, and delivery of complex AI initiatives from problem framing and architecture to production rollout ensuring clear business impact and measurable outcomes.
• Technical Thought Leadership: Act as a hands-on technical authority in ML, Deep Learning, NLP, Generative AI, and Agentic AI, guiding solution design for intelligent automation, decision intelligence, and autonomous systems.
• Intelligent Automation & AI Systems: Architect and drive AI-powered automation solutions using predictive models, LLMs, and agent-based workflows to solve challenging, high-value enterprise problems.
• Innovation & R&D: Continuously evaluate emerging AI trends, tools, and research (e.g., multi-agent systems, RAG, LLM fine-tuning, Reasoning models) and apply them pragmatically to real-world use cases.
• Team Leadership & Mentorship: Build, mentor, and inspire high-performing AI teams, providing technical direction, code/design reviews, and career guidance for data scientists and ML engineers.
• Cross-Functional Collaboration: Work closely with product, engineering, and business stakeholders to identify AI opportunities, define solution roadmaps, and ensure seamless integration into enterprise systems.
• Quality, Reliability & MLOps: Ensure AI solutions meet high standards of accuracy, robustness, scalability, and observability through strong MLOps practices and continuous monitoring.
• Strategic Contribution: Contribute to enterprise AI strategy, capability building, and long-term roadmaps aligned with organizational growth and innovation goals.

Qualifications & Experience

• Bachelor s degree in Computer Science, Engineering, Mathematics, or a related field; Master s or PhD preferred.
• 6+ years of hands-on experience delivering AI/ML solutions, with strong depth in classical ML, deep learning, and NLP.
• Proven experience leading and delivering multiple large-scale AI projects from idea to production across diverse domains.
• Strong expertise in ML frameworks such as PyTorch, TensorFlow, Scikit-learn, and modern deep learning architectures.
• Demonstrated experience with Generative AI and LLMs (e.g., GPT, BERT, Transformers), including RAG, fine-tuning, and agent-based systems.
• Solid experience with MLOps, Databricks, and cloud-native AI platforms (Azure, AWS, GCP).
• Strong understanding of designing AI systems for intelligent automation, decision support, and autonomous workflows.
• Excellent leadership, communication, and stakeholder-management skills.
• Strong analytical mindset with a proven ability to solve ambiguous, high-impact problems in fast-paced environments.