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AI/ML Lead

Weekday
Pune
On-siteUSD 12 - 27 / year

About this role

AI/ML Lead

Location: Pune, India Experience: 7–10 Years Function: Information Technology / Digital Transformation Reporting To: CIO / Head of Digital Transformation / VP – IT Employment Type: Full-Time About Millennium Semiconductors Millennium Semiconductors is a leading B2B electronics distribution organization serving OEMs, ODMs and EMS companies across automotive, telecom, consumer electronics and industrial automation. With a growing global footprint and a strong focus on digital transformation, the organization is investing in AI, GenAI, analytics and technology-led business transformation. Role Purpose The AI Lead / Manager – Artificial Intelligence & Data Science will lead the organization's AI and GenAI transformation agenda, from identifying high-value business use cases through solution design, POC, deployment and enterprise adoption. The role will act as a strategic bridge between business, data and technology, enabling functions such as Sales, Product Marketing, Demand Creation, Supply Chain, Customer Service, HR, Finance and Corporate Functions to leverage AI for improved productivity, decision-making, customer experience and business growth.

Key Responsibilities

    1. AI Strategy & Roadmap

  • Develop and execute the enterprise AI/GenAI roadmap aligned with business priorities.

  • Identify, evaluate and prioritize high-impact AI/ML and GenAI use cases.

  • Build business cases, ROI models, investment proposals and implementation plans.

  • Define AI maturity frameworks and establish priorities across business functions.

  • Track emerging AI technologies and assess their applicability to the organization.

    1. AI & GenAI Solution Delivery

  • Lead AI initiatives from ideation and POC through production deployment and scaling.

  • Drive development of predictive models, machine learning solutions, NLP, GenAI and intelligent automation applications.

  • Define solution architecture in collaboration with IT, data engineering and technology teams.

  • Establish frameworks for model development, testing, deployment, monitoring and continuous improvement.

  • Ensure AI solutions are scalable, reliable, secure and business-ready.

  • Evaluate and leverage LLMs, RAG, AI agents, APIs, vector databases and other relevant AI technologies.

    1. Business Partnership & Transformation

  • Partner with Sales, Marketing, Product Marketing, Supply Chain, Finance, HR, Customer Service and Corporate Functions.

  • Translate business challenges into actionable AI use cases and solution requirements.

  • Facilitate AI discovery workshops and identify opportunities for process transformation.

  • Drive adoption of AI solutions and ensure measurable business outcomes.

  • Support change management, user enablement and AI literacy across the organization.

    1. Data, AI Governance & Responsible AI

  • Work closely with data engineering and IT teams to ensure data quality, availability and readiness.

  • Establish standards for AI governance, model risk management, security and compliance.

  • Define policies around responsible use of AI and GenAI.

  • Ensure transparency, explainability, data privacy and appropriate controls across AI solutions.

  • Establish processes for model performance monitoring, drift detection and continuous optimization.

    1. AI Centre of Excellence

  • Establish and evolve an AI Centre of Excellence (AI CoE).

  • Define AI frameworks, standards, reusable components and best practices.

  • Build a structured pipeline of AI use cases across business functions.

  • Develop governance mechanisms to monitor AI investments, adoption and business impact.

  • Create an internal knowledge base and promote AI adoption across the organization.

    1. Team & Stakeholder Leadership

  • Build, lead and mentor AI, Data Science and Analytics talent.

  • Define team structure, capabilities and development plans.

  • Manage external AI consultants, technology partners and vendors.

  • Evaluate AI platforms, tools and technology partners.

  • Present AI strategy, progress, business cases and outcomes to senior leadership.

  • Key Success Measures / KPIs

  • AI roadmap development and execution.

  • Number and value of AI/GenAI use cases identified and deployed.

  • POC-to-production conversion rate.

  • Business ROI and cost/productivity improvements delivered through AI.

  • Reduction in manual effort and process cycle time.

  • User adoption and utilization of AI solutions.

  • AI solution reliability, accuracy and performance.

  • Data and AI governance compliance.

  • Development of internal AI capabilities and CoE maturity.

  • Candidate Profile

Education

  • Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, Statistics, Mathematics or a related discipline.
  • MBA or business-oriented qualification would be an advantage.

Experience

  • 7–10 years of relevant experience in AI, Machine Learning, Data Science, Advanced Analytics or Digital Transformation.

  • Proven experience leading AI/ML projects from business problem identification to production deployment.

  • Strong exposure to Generative AI, LLMs and AI-driven business transformation.

  • Experience working with cross-functional business and technology stakeholders.

  • Experience managing AI teams, technology partners or external consultants.

  • Technical Expertise

  • Artificial Intelligence and Machine Learning

  • Generative AI and Large Language Models (LLMs)

  • NLP and predictive analytics

  • RAG and AI Agent frameworks

  • Python and relevant AI/ML libraries

  • Cloud AI platforms – AWS / Azure / GCP

  • Data platforms, APIs and modern data architectures

  • Model deployment, MLOps and AI monitoring

  • AI governance, security and Responsible AI

  • Leadership Competencies

  • Strategic Thinking: Ability to translate emerging AI capabilities into business value.

  • Business Acumen: Strong understanding of commercial and operational processes.

  • Technology Leadership: Ability to evaluate and implement modern AI technologies.

  • Stakeholder Management: Strong communication with C-suite, business leaders and technical teams.

  • Execution Orientation: Ability to move initiatives from POC to scalable production solutions.

  • Innovation Mindset: Continuously evaluates emerging AI technologies and their business applicability.

  • People Leadership: Builds high-performing AI and Data Science teams.

  • Change Leadership: Drives enterprise adoption and organizational transformation.

  • Must-have skills

  • Artificial IntelligenceGenerative AIProcess Implementation

  • Good-to-have skills

  • Data ScienceAdvanced AnalyticsRAG

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