Manager – ML Engineering

Location: Bengaluru

Key Deliverables in this role

MLOps & Production ML Pipelines
Design and implemented-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 ML flow, 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.

Product ionising & Scaling ML Models

Deploy and serve ML models at scale using AWS Sage Maker, Sage Maker Endpoints, or equivalent managed ML platforms.
Optimize model inference for latency, throughput, and cost - including quantization, distillation, batching strategies, and GPU/CPU optimization.
Containerize 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.

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

Desirable success factors for the role
Exposure to modern data engineering concept and data preparation to create ML-ready data sets
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 / Snap Logic.

Core Technical Skills
Python :
Strong proficiency; clean, production-quality code with solid software engineering practices (OOP, design patterns, testing).
MLOps Tools:
Production experience with MLflow, DVC, Kubeflow, or W&B for experiment tracking, model registry, and versioning.
Cloud ML platforms:
Hands-on with AWS Sage Maker(Training, Endpoints, Pipelines, Registry) or equivalent (GCP Vertex AI, Azure ML).
Model Lifecycle:
End-to-end ownership — data exploration, experimentation, training, evaluation, deployment, monitoring, and retraining.
Containerization:

Experience with Docker and Kubernetes for packaging and deploying ML workloads.
CI / CD for ML:
Experience building

CI/CD pipelines (Git Hub Actions , Jenkins, GitLabCI) integrated with ML workflows.
Version Control:
Proficient with Git; experience with Git-based workflows for code, data, and model artifacts.