๐ง๐ต๐ถ๐ ๐ฟ๐ผ๐น๐ฒ ๐ถ๐ ๐ณ๐ผ๐ฟ ๐ผ๐ป๐ฒ ๐ผ๐ณ ๐๐ต๐ฒ ๐ช๐ฒ๐ฒ๐ธ๐ฑ๐ฎ๐'๐ ๐ฐ๐น๐ถ๐ฒ๐ป๐๐
๐ฆ๐ฎ๐น๐ฎ๐ฟ๐ ๐ฟ๐ฎ๐ป๐ด๐ฒ: ๐ฅ๐ ๐ญ๐ฎ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ - ๐ฅ๐ ๐ฎ๐ณ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ (๐ถ๐ฒ ๐๐ก๐ฅ ๐ญ๐ฎ-๐ฎ๐ณ ๐๐ฃ๐)
Experience: 7+ yrs
Location: Pune
As the AI Lead / Manager, you will collaborate with cross-functional teams, including Sales, Marketing, Supply Chain, Finance, HR, Customer Service, and IT, to identify business challenges and translate them into scalable AI-driven solutions.
You will be responsible for defining the AI roadmap, establishing an AI Centre of Excellence (CoE), implementing responsible AI practices, and building internal capabilities to support long-term digital transformation. The role also involves leading AI talent, managing technology partners, and ensuring measurable business outcomes through successful AI adoption.
Requirements
Key Responsibilities
- AI Strategy & Roadmap:ย Develop and execute the enterprise AI and GenAI roadmap aligned with business objectives. Identify high-value use cases, build business cases, and define implementation priorities based on ROI and business impact.
- AI & GenAI Solution Delivery:ย Lead AI/ML initiatives from ideation and POC to production deployment. Drive solutions using machine learning, predictive analytics, NLP, LLMs, RAG, AI agents, and intelligent automation.
- Business Collaboration:ย Partner with business and technology stakeholders to identify opportunities, translate business requirements into AI solutions, and drive adoption across functions.
- Solution Architecture & Optimization:ย Collaborate with IT and data engineering teams to design scalable, secure, and reliable AI solutions. Establish standards for model development, deployment, monitoring, and continuous improvement.
- Data Governance & Responsible AI:ย Define frameworks for AI governance, data privacy, model risk management, security, explainability, and regulatory compliance.
- AI Centre of Excellence:ย Establish and strengthen the AI CoE by developing reusable frameworks, best practices, governance mechanisms, and a structured pipeline of AI initiatives.
- Team Leadership:ย Build, mentor, and lead AI, Data Science, and Analytics teams. Manage external consultants, technology partners, and vendors while evaluating emerging AI platforms and tools.
- Business Impact & Adoption:ย Track AI performance through measurable KPIs, including ROI, productivity improvements, process efficiency, solution accuracy, user adoption, and POC-to-production conversion.
- Executive Stakeholder Management:ย Present AI strategies, investment proposals, progress updates, and business outcomes to senior leadership.
What Makes You a Great Fit?
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, Statistics, Mathematics, or a related field. An MBA or business-oriented qualification is an added advantage.
- 7โ10 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 through development, deployment, and production scaling.
- Strong expertise in Generative AI, LLMs, NLP, predictive analytics, RAG, and AI agent frameworks.
- Hands-on proficiency in Python and relevant AI/ML libraries, along with experience using cloud AI platforms such as AWS, Azure, or GCP.
- Good understanding of modern data architectures, APIs, vector databases, MLOps, model deployment, and AI monitoring.
- Experience working with cross-functional stakeholders, managing AI teams, and coordinating with external technology partners or consultants.
- Knowledge of AI governance, responsible AI principles, data security, privacy, and model risk management.
- Strong strategic thinking and business acumen, with the ability to translate AI capabilities into measurable business value.
- Excellent communication, stakeholder management, problem-solving, and people leadership skills.
- A proactive, innovation-driven mindset with the ability to move AI initiatives from experimentation to scalable, production-ready solutions and drive enterprise-wide adoption.