Lead AI/ML Engineer

Primary Responsibilities:

- Own end-to-end AI/ML development for GICS (Global Infrastructure and Cloud Services) initiatives, spanning problem formulation, data exploration, model selection, training, evaluation, deployment, and monitoring in production
- Design and evaluate machine learning models using rigorous statistical and ML metrics (e.g., precision/recall, F1 score, ROC AUC), ensuring models are fit for business-critical workflows
- Develop and operationalize LLM-based systems with a robust understanding of model behavior, limitations, hallucination risk, bias, fairness, and explainability beyond simple prompt engineering
- Assess and mitigate bias, fairness, and ethical risks in ML and LLM solutions, aligning with Responsible AI principles and enterprise governance expectations
- Build production-quality AI services and pipelines, leveraging strong software engineering fundamentals (Python, APIs, microservices, data pipelines, model serving, CI/CD)
- Translate research and experimentation into scalable solutions, balancing experimental rigor with real-world performance, reliability, and maintainability
- Partner with architects, product managers, and platform teams to ensure AI solutions integrate cleanly with enterprise systems (data platforms, observability, security, and APIs)
- Lead technical design decisions for AI/ML components, serving as a subject matter expert and reviewer for model choices, evaluation approaches, and implementation quality




- Monitor and improve models post-deployment, establishing feedback loops, retraining strategies, performance dashboards, and drift detection where appropriate
- Mentor other engineers and data scientists across CIOP AI by setting high technical standards, reviewing work, and promoting best practices in ML engineering and Responsible AI

Required Qualifications:

- Master s degree in Data Science, Computer Science, or related field; PhD preferred
- 8+ years of experience in AI/ML engineering, including model development and deployment
- Hands-on experience building, evaluating, and deploying ML models in production, with clear understanding of evaluation metrics such as precision, recall, F1 score, and tradeoffs between them
- Experience with building Agentic AI application, using frameworks such as Langchain, Langgraph and autogen
- Deep foundation in machine learning and data science, including statistics, probability, optimization, and modern ML techniques (supervised, unsupervised, deep learning)
- Proven expertise with LLMs and NLP systems, including model behavior analysis, bias/fairness considerations, and techniques to improve reliability and accuracy beyond prompt tuning

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