AI/ML Manager

Job Title: AI/ML Manager Company: Third eye info technologies Job Category: Data Science Roles & Responsibilities: Job Description: AI/ML Manager – Enterprise AI Implementation Location: Malaysia Candidate Sourcing: India Experience: 8–15\+ years overall experience, including significant experience leading AI/ML initiatives Role Level: Senior Manager / AI Transformation Manager / Head of AI Implementation (designation based on experience) About the Role We are seeking an experienced AI/ML Manager with proven experience in enterprise\-level Artificial Intelligence implementation to lead the strategy, planning, development, deployment, and scaling of AI\-driven solutions across business functions. The ideal candidate will have a strong combination of AI/ML technical knowledge, enterprise technology leadership, stakeholder management, and program delivery experience. This role requires a professional who has successfully taken AI initiatives beyond proof of concept (PoC) and pilots into production environments, delivering measurable business value, operational efficiency, and scalable enterprise capabilities. The candidate will work closely with executive leadership, business stakeholders, IT teams, data engineering teams, and external technology partners to identify AI opportunities, establish implementation roadmaps, and drive enterprise\-wide adoption of AI solutions. Key Responsibilities * Develop and execute enterprise AI strategies aligned with organizational business objectives. * Identify high\-impact opportunities for AI/ML adoption across business functions, operations, products, and customer\-facing applications. * Define AI implementation roadmaps, investment priorities, business cases, and delivery milestones. * Evaluate AI use cases based on business value, technical feasibility, scalability, risk, and expected return on investment. * Lead the transition from AI experimentation and proof of concept to production\-grade enterprise implementations. * Establish standards, frameworks, and best practices for enterprise AI adoption. 2\. Enterprise AI Implementation \& Delivery * Lead end\-to\-end implementation of AI/ML solutions across multiple business units or enterprise functions. * Oversee AI programs from requirements gathering and solution architecture through development, integration, deployment, and ongoing optimization. * Manage large\-scale AI implementation projects involving multiple teams, technology platforms, vendors, and stakeholders. * Ensure AI solutions integrate effectively with existing enterprise applications, data platforms, APIs, and business workflows. * Drive the deployment of scalable, secure, reliable, and maintainable AI systems. * Track implementation progress, resolve delivery challenges, manage dependencies, and ensure successful project outcomes. * Establish clear success metrics and measure the business impact of deployed AI solutions. * Lead the evaluation and implementation of Generative AI and Large Language Model (LLM) solutions for enterprise use cases. * Drive the adoption of enterprise AI applications such as intelligent assistants, knowledge management systems, document intelligence, conversational AI, workflow automation, and decision\-support systems. * Oversee initiatives involving Retrieval\-Augmented Generation (RAG), enterprise search, AI agents, model orchestration, and integration with internal knowledge repositories, where relevant. * Evaluate commercial AI platforms, foundation models, and open\-source technologies based on business needs, security, cost, performance, and scalability. * Define approaches for responsible LLM deployment, model evaluation, output quality, and risk management. * Establish appropriate controls for sensitive enterprise data, access permissions, hallucination risks, and human oversight. 4\. AI/ML Program \& Team Management * Lead and manage cross\-functional teams comprising AI/ML engineers, data scientists, data engineers, software developers, architects, and business analysts. * Define project scope, resource plans, budgets, timelines, deliverables, and governance structures. * Build and mentor high\-performing AI teams and develop internal capabilities for sustainable AI adoption. * Manage external implementation partners, consultants, and technology vendors where applicable. * Establish delivery standards, review mechanisms, performance indicators, and escalation processes. * Communicate program progress, risks, dependencies, and outcomes to senior management and executive stakeholders. * Work with enterprise architects, IT leaders, and data leaders to define scalable AI solution architectures. * Guide decisions regarding cloud\-based AI platforms, data infrastructure, model deployment, enterprise integration, and technology selection. * Ensure AI solutions align with enterprise architecture, application security, infrastructure, and data management standards. * Promote appropriate MLOps, LLMOps, model lifecycle management, monitoring, and continuous improvement practices. * Establish governance processes for model validation, performance monitoring, versioning, and production support. * Establish governance frameworks for enterprise AI implementation and adoption. * Ensure AI solutions adhere to applicable data privacy, information security, regulatory, and organizational policies. * Address risks related to data leakage, model bias, explainability, reliability, intellectual property, and unauthorized AI usage. * Work with security, compliance, legal, and risk teams to define appropriate controls and approval processes. * Promote responsible, transparent, and auditable AI practices across the organization. * Partner with business leaders to redesign processes and identify opportunities for AI\-led transformation. * Quantify business benefits, including productivity improvements, cost optimization, faster decision\-making, and enhanced customer experience. * Develop business cases and establish KPIs to measure AI adoption, operational impact, and return on investment. * Drive change management, user adoption, training, and organizational readiness for AI\-enabled workflows. * Present implementation outcomes, strategic recommendations, and progress reports to executive leadership. * Enterprise AI implementation and transformation. * AI/ML program and project management. * AI strategy, use\-case prioritization, and roadmap development. * Production deployment and scaling of AI solutions. * Stakeholder and executive management. * Enterprise solution architecture and systems integration. * Team leadership, resource planning, and delivery governance. * Business case development, KPI tracking, and ROI measurement. * AI risk management, governance, privacy, and security awareness. Candidates should have practical implementation or leadership exposure to several of the following: * Enterprise Generative AI and LLM platforms. * RAG\-based applications, enterprise search, and AI assistants. * Machine Learning, Deep Learning, NLP, and predictive analytics. * Microsoft Azure AI, AWS AI services, or Google Cloud AI. * Enterprise data platforms, data lakes, data warehouses, and data engineering. * MLOps, LLMOps, model monitoring, and deployment automation. * AI application integration with ERP, CRM, enterprise knowledge systems, and business applications. * AI agents, workflow automation, and intelligent process automation. * Commercial AI platforms and open\-source AI ecosystems. * Delivering at least one or more significant AI implementations in a complex enterprise environment. * Moving AI solutions from experimentation to production and sustained business use. * Managing multiple stakeholders, teams, and technology dependencies. * Establishing effective AI governance, security, and operational controls. * Demonstrating measurable business value from AI investments. * Driving adoption of AI solutions across teams, departments, or business units. We are looking for a strategic yet execution\-oriented technology leader who understands both the business and technical dimensions of enterprise AI. The ideal candidate is not limited to AI research, model development, or proof\-of\-concept experimentation. They should have direct ownership or substantial leadership responsibility for implementing, integrating, deploying, governing, and scaling AI solutions in a real enterprise environment. Work Location: Remote Work Location: Remote, India Availability: Full time

Candidates must have demonstrable experience managing large\-scale AI implementations in complex business or enterprise environments. Required Experience \& Qualifications * Bachelor's or Master's degree in Computer Science, Information Technology, Artificial Intelligence, Data Science, Engineering, or a related discipline. * Approximately 8–15\+ years of overall technology, data, digital transformation, or AI\-related experience, with the level adjusted to the complexity of prior enterprise implementations. * Significant experience managing or leading enterprise AI/ML implementation programs. * Demonstrated success taking AI initiatives from business requirements and PoCs into production deployment and scaled adoption. * Experience managing multidisciplinary technical teams and complex technology delivery programs. * Strong understanding of AI/ML concepts, enterprise data platforms, cloud technologies, and modern AI architectures. * Experience engaging with senior business stakeholders, IT leadership, and executive management. * Proven ability to connect technology investments with measurable business outcomes. Application Requirements Key Skills: Data Science Salary: INR 405000 - 1.67e+06 per yearly