Manager - ML Engineering

Function: Digital

Location: Bangalore

Reporting To: Vice President – Data & AI

Top dimensions :

Geography: Global

Direct reports: - NA

Complexity of the role (Optional):

Matrix Reports : NA

Type of Role : Individual Contributor

Primary Stakeholders (Optional):

What are the Key Deliverables in this role ?

MLOps & Production ML Pipelines

• Design and implement end-to-end MLOps pipelines covering data ingestion, feature engineering, model training, evaluation, deployment, and monitoring.
• Build and maintain CI/CD pipelines for ML models with automated testing, validation, and rollback capabilities.
• Implement experiment tracking, model versioning, and data versioning using tools such as MLflow, DVC, Weights & Biases, or similar.
• Manage model registry workflows - promoting models from experimental → staging → production with governance and approval gates.
• Automate reproducible training runs with parameterized configs, seed management, and environment pinning.

Productionising & Scaling ML Models

• Deploy and serve ML models at scale using AWS SageMaker, SageMaker Endpoints, or equivalent managed ML platforms.
• Optimise model inference for latency, throughput, and cost - including quantisation, distillation, batching strategies, and GPU/CPU optimisation.
• Containerise ML workloads using Docker and orchestrate with Kubernetes for reliable, reproducible deployments.
• Build and maintain RESTful / gRPC model serving APIs with proper error handling, authentication, and rate limiting.

Monitoring, Observability & Reliability

• Set up continuous monitoring for data drift, model drift, and performance degradation in production - critical in FMCG where consumer behaviour and market dynamics shift rapidly.
• Build alerting and automated retraining pipelines triggered by drift detection or performance thresholds.
• Ensure model observability through logging, metrics dashboards, and explainability tooling.
• Define and track ML-specific SLOs/SLAs (latency p50/p95/p99, throughput, accuracy, freshness).

Infrastructure & Tooling

• Maintain and improve internal ML platforms and tooling to accelerate team productivity.
• Drive best practices for reproducible experimentation, code quality, testing, and documentation across the ML team.
• Evaluate and integrate emerging tools and frameworks into the AI stack as the ecosystem evolves.

What are the Critical success factors for the Role ?

• 3+ years of hands-on experience in Machine Learning / Deep Learning engineering in production environments.
• Demonstrated track record of taking AI/ML models from prototype to production at scale.

• Experience in FMCG, consumer goods, supply chain, or retail domains is a strong plus.

What are the Desirable success factors for the Role?

• Exposure to modern data engineering concept and data preparation to create ML-ready datasets
• Familiarity with data governance / metadata practices (catalog, lineage, stewardship).
• Experience with additional AWS services (e.g., Lambda, Redshift) or Azure/GCP.
• Relevant certifications: AWS / Snowflake / SnapLogic.

Core Technical Skills