AI- Technical Manager

Role Purpose
The AI- Technical Manager owns the technical and project delivery of AI initiatives for AMETEK India Engineering and its Business Units, taking each from concept through proof of concept and validation to controlled deployment. The role is technology- and product-passionate: it looks at how AMETEK products and engineering workflows are built, tested and supported, identifies where AI can remove effort, cycle time or rework, and turns those opportunities into solutions that deliver measurable business ROI and can be reused across BUs.
Key Responsibilities
1. Opportunity Identification and Business Value
Engage BU engineering teams to find high-value opportunities across the product lifecycle (design, software, hardware and firmware, validation, quality, service) where AI can improve efficiency.
Build the business case with the BU: baseline, expected benefit, effort and cost; prioritise with Engineering Leadership, BU Leads and Domain AI Leads.
Track realised benefits against the agreed baseline and report ROI to stakeholders.
Technology incubation: be familiar with the latest industry AI tools and technologies and drive deployment as per required usage scenarios.
2. Project Delivery
Own assigned projects end to end: scope, plan, milestones, resources, budget and acceptance criteria, delivered to completion on schedule.
Responsible for developing, deploying and driving wider adoption of AI solutions across AMETEK Engineering and its BUs.
Manage risks, dependencies and changes; escalate early with options and secure timely decisions.
Run stage gates and readiness reviews, and hand over deployed solutions with documentation, support ownership and user training.
Manage vendors and partners engaged on the project, including scope, deliverables and cost.
3. Technical Leadership
Define solution architecture and select models, platforms and deployment approaches (cloud, on-premises, hybrid, local LLM) that balance capability, security, performance and cost.
Design and review solutions using Generative AI, RAG, AI agents, machine learning and semantic search, integrated with engineering data and tools.
Provide practical technical solutions to challenges faced in AI adoption across all engineering domains.
Define evaluation methods and acceptance criteria for accuracy, reliability, security, usability and cost; ensure source traceability and human validation.
Implement guardrails and remain 100% compliant with AMETEK IT policies and governance model, including data-classification, export-control and customer-IP requirements, working with IT, Cybersecurity and Responsible AI governance.
Build reusable components and patterns so validated solutions can be scaled to other AMETEK BUs.
4. Team Leadership
Lead and mentor the Engineers and interns within AI team: work packages, code and design reviews, performance feedback and development.
Continuous development: train and ramp up the AI technical team on the latest industry trends, tools and technologies.
5. Reporting
Define, measure and report KPIs for AI.
Create a dashboard and update all stakeholders at pre-defined intervals.
Communicate progress, technical risks and resource needs to engineering leadership and BU stakeholders.
Qualifications and Experience
Bachelor's or Master's degree in Computer Science, AI/ML or a related engineering discipline, project management certification is preferred.
10+ years in software, product engineering, solution architecture or AI/ML, including 3+ years leading teams and managing technical projects.
Proven hands-on experience implementing AI/ML solutions in past roles, demonstrating validation against defined acceptance criteria and scalability from prototype to controlled production deployment and wider reuse.
Track record of delivering projects to scope, schedule and budget with measurable business outcomes.
Experience in a product company or multidisciplinary engineering environment; regulated industry exposure is an advantage.
Core skills
Technical – AI
Generative AI, LLMs, RAG, embeddings, vector search
AI agents and agentic workflows
Python and AI/ML frameworks
Model, retrieval and agent evaluation
APIs, containers, CI/CD, MLOps, observability
Local LLM deployment, GPU, cloud and hybrid architecture
Secure integration with engineering and enterprise systems
Project and People Management
Planning, scheduling, scope and dependency management
Risk, issue and change management
Budget and vendor management
Stage-gate / Agile delivery; benefits tracking
Stakeholder management and status reporting
KPI definition, dashboards and reporting
Coaching, performance feedback, team development
Ecosystems may include Microsoft Copilot, Azure AI, OpenAI, Anthropic Claude, Ollama, Llama, Mistral, PyTorch, LangChain, Semantic Kernel and Hugging Face; Jira, Azure DevOps or MS Project. Experience with every item is not required.
Preferred Profile
Passionate about technology and products, with a curiosity for how things are built and where AI can make them better.
Hands-on and credible in reviewing critical code, with strong systems thinking and problem solving.
Disciplined in planning and follow-through; focused on delivered outcomes and ROI rather than demonstrations.
Effective mentor and communicator with engineering and leadership stakeholders across locations.