Manager - AI/ML

Nidec Corporation, headquartered in Kyoto, Japan is known for advancements in motor technologies and its application in various industries. Nidec Advance Technology India, contribute to manufacturing by measurement and inspection technology.

www.nidec.com/en/nidec-advancetechnology

https://youtu.be/D40vFepfYfw?si=YwDT78Xx0xOMGBIV

This is a full-time on-site role located in Bengaluru for a Manager - AI/ML. The individual will lead and manage AI/ML projects, including data analysis, predictive modeling, and algorithm development. Responsibilities include collaborating with cross-functional teams, ensuring data-driven decision-making, and overseeing the implementation of machine learning processes to meet business objectives. The role will also involve mentoring team members and fostering innovation in AI/ML development.

This is 'Work from Office' position only, Mon-Fri 9AM to 6PM

Work Location: Nidec Advance Technology India (NATI)

Workhub by Novel, 2nd F, Plot No.37,21&24, Doddanakundi Industrial Area 2, Phase 1,

ITPL Road Doddanekkundi, Bengaluru, Karnataka 560048

600 mtrs from Hoodi Metro Station

Key Responsibilities

AI/ML Program & Project Leadership

Lead large-scale AI/ML programs from problem definition to production

deployment

Own delivery across data acquisition, labeling, model development, validation,

deployment, and monitoring

Define project scope, milestones, KPIs, risks, and success metrics

Manage multiple concurrent AI initiatives across teams and geographies

AI/ML Lifecycle Management

Oversee:

o Data strategy (collection, labeling, quality, governance)

o Model training, evaluation, iteration, and performance benchmarking

o Inference optimization and deployment (edge / cloud / hybrid)

o Model monitoring, drift detection, retraining strategy

Ensure reproducibility, scalability, and compliance across AI pipelines

Stakeholder & Business Alignment

Act as the bridge between business, product, and technical teams

Translate business problems into AI/ML requirements and roadmaps

Present progress, risks, and outcomes to executive leadership

Manage expectations around AI feasibility, timelines, and ROI

System Design & Architecture Oversight

Collaborate with architects to define end-to-end AI system design

Review and guide decisions on:

o Data pipelines

o Model serving architectures

o MLOps frameworks

o Infrastructure (GPU, cloud, edge, CI/CD)

Ensure solutions meet performance, reliability, and security standards

Governance, Risk & Compliance

Establish AI governance frameworks (versioning, auditability, documentation)

Ensure compliance with data privacy, security, and ethical AI standards

Manage vendor partnerships, tooling, and third-party AI platforms

Risk Management:

o Identify technical risks early (e.g., API latency issues, data pipeline

failures) and engineer mitigation plans.

Uncertainty Management:

o Manage the unique risks of AI projects (e.g., model drift, lack of quality

data, experimental failure) by implementing agile "fail-fast"

mechanisms and Proof of Concept (PoC) stage-gates.

Team Leadership & Mentorship

Lead and mentor project managers, ML engineers, and analysts

Drive best practices in Agile / Hybrid delivery models for AI projects

Build a culture of accountability, experimentation, and continuous

improvement

Required Qualifications

Education

PhD in Engineering, Computer Science, Data Science, or related field

MBA or advanced management qualification is a plus

Experience

1015 years of overall experience, with 5+ years in AI/ML-driven projects

Proven track record of delivering production-grade AI systems

Experience across multiple AI domains (Computer Vision / Generative AI /

NLP / Time Series Forecasting / Recommendation Systems / Predictive

Analytics)

Technical & Domain Knowledge

AI/ML Understanding (Hands-on familiarity preferred)

Machine Learning & Deep Learning fundamentals

Model training, evaluation metrics, and trade-offs

Data labelling strategies and quality management

MLOps concepts (CI/CD, monitoring, retraining)

Inference optimization and scalability challenges

Tools & Platforms (Awareness / Experience)

ML frameworks: PyTorch, TensorFlow (conceptual understanding sufficient)

Data tools: SQL, data lakes, feature stores

Cloud platforms: AWS / Azure / GCP

Experiment tracking & MLOps tools (MLflow, Kubeflow, etc.)

Project & Leadership Skills

Strong expertise in Agile, SAFe, and hybrid delivery models

Excellent risk management and dependency tracking skills

Exceptional communication and executive-level presentation skills

Ability to manage ambiguity in research-heavy AI initiatives

Strong decision-making with a balance of speed and rigor

Preferred Qualifications

Experience managing AI products, not just projects

Exposure to regulated industries (automotive, manufacturing)

Background in scaling AI from PoC to enterprise rollout

What Success Looks Like

AI projects delivered on time, within scope, and in production

Measurable business impact from AI initiatives

Strong collaboration across business and technical teams

• Robust, scalable, and governable AI systems