New opportunity

AI- Technical Manager

AMETEK, Inc. · Bangalore, India

About this role

The AI Technical Manager will own the technical and project delivery of AI initiatives for AMETEK India Engineering, guiding projects from concept through proof of concept, validation, and controlled deployment. The role focuses on identifying opportunities within product lifecycles—including design, software, hardware, firmware, and quality—where AI can improve efficiency, reduce cycle time, and deliver measurable ROI. Responsibilities include defining solution architecture, selecting appropriate models and platforms, and ensuring compliance with security and governance policies. The manager will lead and mentor a team of engineers and interns, define KPIs, and report progress to engineering leadership. Required Qualifications: Bachelor's or Master's degree in Computer Science, AI/ML, or a related engineering discipline. Minimum 10 years of experience in software, product engineering, solution architecture, or AI/ML, including at least 3 years in team leadership and technical project management. Proven hands-on experience implementing AI/ML solutions and scaling them from prototype to production. Preferred Qualifications: Project management certification and experience in a product company or regulated industry. Technical Skills: Proficiency in Generative AI, LLMs, RAG, AI agents, Python, MLOps, and various deployment architectures. Familiarity with ecosystems such as Azure AI, OpenAI, Anthropic, PyTorch, LangChain, and Semantic Kernel is beneficial. The role requires strong systems thinking, disciplined planning, and the ability to communicate effectively with stakeholders across locations.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Generative AILLMsRAGEmbeddingsVector searchAI agentsPythonAI/ML frameworksModel evaluationRetrieval evaluationAgent evaluationAPIsContainersCI/CDMLOpsObservabilityLocal LLM deploymentGPUCloud architectureHybrid architectureProject managementTeam leadershipStakeholder managementBudget managementVendor managementAgile delivery

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Driving Measurable AI ROI
The primary mandate is to identify high-value opportunities across the product lifecycle—including design, firmware, and quality—and translate them into solutions that deliver quantifiable business outcomes. You must be able to build business cases, establish baselines, and track realized benefits against costs to report ROI to engineering leadership.
End-to-End Project Delivery
You are responsible for the full lifecycle of AI initiatives, from initial concept and proof of concept to controlled deployment. This requires disciplined planning, managing stage-gate reviews, handling dependencies, and ensuring that solutions are documented, supported, and ready for wider adoption across different business units.
Technical Architecture and Governance
You must define robust solution architectures that balance performance, cost, and security. This includes selecting appropriate models and deployment approaches (cloud, hybrid, or local) while ensuring 100% compliance with IT policies, data-classification standards, and export-control requirements.
Team Leadership and Mentorship
You will lead and mentor a team of engineers and interns, which involves conducting code and design reviews, providing performance feedback, and driving continuous development to keep the team updated on the latest AI trends and tools.

Likely challenges to think through

Balancing Innovation with Strict Governance
Operating within a regulated industrial environment requires implementing advanced AI like LLMs and agents while adhering to stringent data-classification and customer-IP protection policies. Prepare by demonstrating how you have successfully navigated security constraints without stifling technical innovation.
Scaling Prototypes to Production
Moving from a successful proof of concept to a reliable, production-grade deployment is a common hurdle. Prepare by discussing your experience with MLOps, observability, and building reusable components that allow a solution to be scaled across diverse business units.
Stakeholder Alignment Across Business Units
You must influence engineering teams and leadership across various locations to adopt new AI workflows. Prepare by highlighting your experience in stakeholder management, communicating technical risks clearly to non-technical leaders, and building consensus for project prioritization.

Practice your answers

Technical practice

Architecting RAG for Engineering Data

What to coverExplain how you would design a Retrieval-Augmented Generation system to integrate with proprietary engineering documentation or firmware codebases. Focus on data chunking strategies, vector database selection, and ensuring source traceability to maintain accuracy and compliance.

Scaling AI from Prototype to Production

What to coverDescribe your approach to transitioning an AI proof-of-concept into a controlled production deployment. Address how you handle MLOps, CI/CD pipelines, and observability to ensure the system remains reliable and performant in a regulated industrial environment.

Evaluating AI Agent Performance

What to coverWhat metrics and methodologies do you use to evaluate the effectiveness of AI agents and LLM-based workflows? Discuss the trade-offs between automated evaluation frameworks and human-in-the-loop validation for engineering-specific tasks.

Hybrid Deployment Strategies

What to coverWhen would you recommend a local LLM deployment versus a cloud-based API solution for an industrial product company? Discuss the implications for data security, latency, cost, and infrastructure requirements.

Ensuring Security and Governance

What to coverHow do you implement guardrails for AI initiatives to ensure compliance with data classification and customer IP requirements? Explain how you balance innovation with the need for strict governance in a corporate engineering setting.

Optimizing AI for Product Lifecycle Efficiency

What to coverProvide an example of how you would identify an opportunity to reduce cycle time in a hardware or firmware development lifecycle using AI. Detail how you would baseline the current process and measure the resulting ROI.

Managing AI Infrastructure and Compute

What to coverDiscuss the considerations for managing GPU resources and model hosting in a hybrid architecture. How do you optimize for cost while ensuring the necessary performance for high-demand engineering AI applications?

Integration with Enterprise Systems

What to coverHow do you approach the secure integration of AI tools with existing engineering software and enterprise systems like Jira or Azure DevOps? Focus on API security, authentication, and maintaining data integrity.

Behavioural practice

Stakeholder Management and ROI Communication

What to coverDescribe a situation where you had to justify the business value of a technical AI project to non-technical leadership. How did you define your KPIs and ensure the project remained aligned with the company's financial and operational goals?

Leading and Mentoring Technical Teams

What to coverGive an example of how you have mentored engineers or interns on new AI technologies. How did you balance their professional development with the need to meet project delivery milestones?

Managing Project Risks and Escalations

What to coverTell me about a time a project faced significant technical or resource-related risks. How did you identify the issue, communicate it to stakeholders, and what options did you present to keep the project on track?

Vendor and Partner Management

What to coverDescribe your experience managing external vendors or partners for a technical delivery. How do you ensure that their deliverables meet your internal quality standards and project requirements?

Sharpen your knowledge

Topics to prepare

AI Solution Architecture and Governance
Focus on designing hybrid deployment architectures that balance cloud, on-premises, and local LLM requirements. Review AMETEK's need for strict compliance with data-classification, export-control, and customer-IP policies. Exercise: Draft a high-level architecture diagram for a RAG-based engineering assistant that ensures data isolation and source traceability.
AI Project Lifecycle and ROI Measurement
Understand the transition from proof-of-concept to controlled production deployment. Focus on defining KPIs that measure efficiency gains and cycle time reduction in engineering workflows. Exercise: Create a project plan template that includes stage-gate reviews, risk management, and a methodology for calculating ROI based on baseline engineering metrics.

Skills to revise

RAG and Agentic Workflow Implementation
Develop proficiency in building systems that integrate engineering data with LLMs using frameworks like LangChain or Semantic Kernel. Exercise: Build a small-scale prototype using Python that retrieves technical documentation from a vector database to answer specific engineering queries, focusing on citation and source accuracy.
MLOps and Model Evaluation
Master the operational side of AI, including CI/CD pipelines, observability, and rigorous evaluation methods for accuracy and reliability. Exercise: Define a set of evaluation criteria for an AI agent, including metrics for retrieval quality and human-in-the-loop validation steps, to ensure the solution meets production standards.
Technical Leadership and Stakeholder Communication
Practice translating complex AI technical risks and progress into actionable reports for non-technical engineering leadership. Exercise: Prepare a mock status report dashboard that highlights project milestones, budget utilization, and technical blockers for a cross-functional stakeholder meeting.

Your preparation checklist

Prepare a portfolio of AI-driven engineering ROI
Develop a structured walkthrough of a past project where you identified an engineering bottleneck, implemented an AI solution (e.g., RAG or agentic workflow), and measured the specific ROI in terms of cycle time reduction or efficiency gains.
Draft a framework for AI governance and security
Create a brief document outlining how you would approach data classification, IP protection, and compliance when deploying LLMs in a regulated, multidisciplinary engineering environment.
Design a scalable MLOps and deployment strategy
Prepare a technical diagram or explanation showing how you would architect a hybrid deployment (cloud/on-prem) for an AI agent, including your approach to observability, model evaluation, and CI/CD pipelines.
Formulate a team leadership and mentorship plan
Outline your approach to managing a technical team, specifically how you would balance hands-on code/design reviews with the need to mentor junior engineers and interns on emerging AI trends.
Prepare strategic questions for leadership
Develop thoughtful questions regarding AMETEK's current engineering data maturity, existing stage-gate processes for AI adoption, and how the company prioritizes AI initiatives across diverse business units.

Availability has not yet been verified. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

KEEP EXPLORING

Similar AI jobs

Related skills and specializations in India. Matched to this role, not your profile.

Explore more jobs

Finding similar opportunities…