Manager - Data Science & AI

Role & responsibilities

• Architect and oversee AI/ML systems, pipelines, and application platforms for enterprise and engineering use cases.
• Set engineering standards and guide technical decisions around model selection, system architecture, infrastructure, observability, security, evaluation, and lifecycle management.
• Provide hands-on technical leadership through design reviews, code reviews, mentoring, and execution guidance for a small AI engineering team.
• Lead the design and implementation of applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), agentic systems, enterprise connectors, and workflow orchestration.
• Translate business and engineering needs into practical AI architectures and phased delivery roadmaps.
• Drive the full lifecycle of AI applications from research and prototyping through evaluation, production deployment, and operational support.
• Ensure AI solutions are explainable, reliable, secure, and aligned with regulatory expectations and internal governance policies.
• Work closely with data engineering, software engineering, IT, and business teams to ensure high-quality, well-governed data availability for AI applications.
• Stay current with advances in AI/ML, evaluate new techniques and frameworks, and drive pragmatic adoption using secure, cloud-native or hybrid architectures.

Preferred candidate profile

• Bachelors / masters degree in computer science engineering with specialization in Data Science/AI/ML/ Statistics

• Overall Experience of 12 to 15 years and minimum 5 years relevant experience

• Experience with open-source model ecosystems, model serving, and deployment optimization.
• Exposure to fine-tuning, adapters, or domain customization techniques.
• Experience designing evaluation frameworks, benchmarks, guardrails, and quality measurement processes.
• Experience with supervised and unsupervised machine learning models.
• Exposure to engineering, industrial, manufacturing, photonics, or other deep-technical domains.
• Experience of managing a highly talented AI/Data Science Team of >=5 people
• Design, build, automate, deploy and maintain machine learning production pipelines.
• Extensive knowledge and skills in frameworks (like TensorFlow, Pytorch, Huggingface), libraries (LlamaIndex, Langchain)
• Expertise in Python and Visualization Python modules like matplotlib, seaborn
• Knowledge of Big data systems, Data ingestion tools/processes/techniques like Streamsets, Spark
• Prior experience leading and mentoring a mid-size (5 people+) technical team.
• Excellent written and verbal communication in English; comfortable presenting to senior leadership / global stakeholders.