Job Title

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./en/nidec-advancetechnologyis 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 560048600 mtrs from Hoodi Metro StationKey ResponsibilitiesAI/ML Program & Project Leadership Lead large-scale AI/ML programs from problem definition to productiondeployment 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 geographiesAI/ML Lifecycle Management Oversee:o Data strategy (collection, labeling, quality, governance)o Model training, evaluation, iteration, and performance benchmarkingo Inference optimization and deployment (edge / cloud / hybrid)o Model monitoring, drift detection, retraining strategy Ensure reproducibility, scalability, and compliance across AI pipelinesStakeholder & 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 ROISystem Design & Architecture Oversight Collaborate with architects to define end-to-end AI system designReview and guide decisions on:o Data pipelineso Model serving architectureso MLOps frameworkso Infrastructure (GPU, cloud, edge, CI/CD) Ensure solutions meet performance, reliability, and security standardsGovernance, 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 pipelinefailures) and engineer mitigation plans.Uncertainty Management: o Manage the unique risks of AI projects (e.g., model drift, lack of qualitydata, 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 continuousimprovementRequired QualificationsEducation PhD in Engineering, Computer Science, Data Science, or related field MBA or advanced management qualification is a plusExperience 10–15 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 / PredictiveAnalytics)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 challengesTools & 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 rigorPreferred Qualifications Experience managing AI products, not just projects Exposure to regulated industries (automotive, manufacturing) Background in scaling AI from PoC to enterprise rolloutWhat 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