AI/ML Engineering Leader

As an AI/ML Engineering Leader , you will architect, scale, and operationalize intelligent enterprise solutions that drive automation, innovation, and measurable business outcomes. This role leads enterprise-wide AI adoption , with a strong focus on Large Language Models (LLMs), Generative AI, Agentic AI, and MLOps , operating across the broader technology and data ecosystem. You will partner closely with business leaders, product owners, data engineering teams, and cloud platform teams to translate strategy into production-grade AI solutions at scale. Key Responsibilities AI Strategy & Leadership Define and execute enterprise AI/ML roadmaps aligned with business KPIs and digital transformation goals. Lead end-to-end AI programs , from ideation and proof-of-concept to production deployment and scaling. Mentor and grow high-performing AI/ML engineering teams , fostering engineering excellence and innovation. Act as a trusted advisor to stakeholders on AI feasibility, value realization, and risk management. Generative AI & LLM Engineering Design and implement Retrieval-Augmented Generation (RAG) architectures leveraging vector databases and enterprise knowledge stores. Build and orchestrate agentic AI workflows using tools such as LangGraph and autonomous decision frameworks. Fine-tune and adapt large language models (LLMs) for domain-specific use cases, optimizing cost, latency, and accuracy. Implement prompt engineering, evaluation frameworks, and guardrails to ensure safe and reliable model behavior. Advanced NLP & AI Applications Develop advanced NLP solutions using transformer-based architectures for document intelligence, chatbots, and knowledge management. Implement AI-driven service transformation solutions on platforms such as ServiceNow , enabling intelligent automation and self-service. Apply Vision AI techniques where applicable for multimodal enterprise use cases. MLOps, CI/CD & Governance Build and maintain scalable MLOps pipelines for training, testing, deployment, and monitoring of models. Establish CI/CD pipelines for ML workflows, ensuring reproducibility and reliability. Implement model monitoring, drift detection, explainability, and auditability . Enforce ethical AI governance , security, privacy, and regulatory compliance standards. Cloud & Data Engineering Architect and deploy AI solutions using AWS SageMaker and AWS Bedrock , following cloud-native best practices. Design scalable ETL and feature engineering pipelines to ensure AI-ready data. Enforce data quality, lineage, and governance standards across structured and unstructured data. Leverage distributed processing frameworks (e.g., Spark) for large-scale data and model workloads. Required Qualifications 15+ years of experience in IT, engineering, or consulting, with 5+ years in AI/ML engineering leadership roles. Deep expertise in Python and R , with hands-on experience using: Pandas for data analysis and feature engineering Scikit-learn for classical machine learning and model evaluation PySpark / Spark for large-scale data processing and ML pipelines Strong hands-on experience with TensorFlow and PyTorch . Proven expertise in LLM fine-tuning, Generative AI, RAG architectures, NLP, and Vision AI . Solid proficiency in MLOps , including CI/CD, model monitoring, explain ability, and lifecycle management. Demonstrated experience with cloud-native AI development on AWS , including Sage Maker SageMaker and Bedrock . Excellent strategic thinking, leadership, mentoring, and stakeholder management skills. Preferred Skills & Attributes Experience designing and implementing agentic AI systems and autonomous workflows. Proven track record of delivering enterprise-scale AI transformations with measurable ROI. Familiarity with ethical AI frameworks , responsible AI practices, and regulatory compliance. Strong understanding of cloud-native AI/ML architecture patterns and cost optimization. Exceptional communication, collaboration, and executive-facing presentation skills.