Join the team as our new MLOps / Platform Engineer (IC3)
India
We are looking for a talented engineer to join our India team - someone who has worked in ML, MLOps, platform engineering and is excited to help build the infrastructure and tooling that empowers our AI transformation. In this role, you will help design, build, and operate systems that support GenAI and ML solutions across the full lifecycle - from data ingestion and model training through to deployment in our self-hosted AI platform.
You will work closely with ML engineers, backend engineers, and platform stakeholders to build systems that are reliable, observable, and built to scale.
Cloud & ML Infrastructure
- Architect and maintain cloud-native ML & GenAI infrastructure on AWS, including managed services such as SageMaker, Bedrock, EKS, EC2, S3, Lambda, API Gateway, RDS, CloudTrail, and CloudWatch.
- Help your team deploy solutions to Kubernetes clusters across cloud and on-premises environments.
- Develop and maintain Infrastructure-as-Code (IaC) using tools such as Terraform and Helm.
Data & Model Pipelines
- Design, build, and manage multi-stage ETL pipelines to support model training and real-time inference workloads.
- Support model development workflows, including experiment tracking, model versioning, and reproducible training runs.
- Collaborate with ML engineers on fine-tuning, evaluation, and deployment of models, including LLMs and GenAI components.
Platform Reliability & Practices
- Implement observability solutions for ML training and inference pipelines (e.g., Weights & Biases or equivalent tooling).
- Establish and enforce platform patterns and engineering best practices across teams.
What It Takes:
Technical
- Proven experience with AWS managed services: SageMaker, Bedrock, EKS, EC2, S3, Lambda, API Gateway, RDS, CloudTrail, and CloudWatch.
- Proven experience with Kubernetes, both on cloud and on-premises.
- Proven experience designing and operating multi-stage ETL pipelines for ML training and inference.
- Proven experience setting up observability for ML models (training and inference), such as with Weights & Biases (W&B).
- Solid understanding of platform engineering best practices and patterns.
- Hands-on experience with Infrastructure-as-Code tools (Terraform, Helm, or similar).
Leadership & Collaboration
- Ability to work closely with ML engineers, backend engineers, and platform stakeholders on shared, cross-functional systems.
- Comfortable establishing and enforcing platform patterns and best practices across teams.
- Clear communicator, able to align infrastructure decisions with the needs of model development and deployment workflows.
Mindset
- Reliability-minded: builds systems that are observable, scalable, and built to last, not just functional.
- Curious about the full ML lifecycle, from data ingestion and training through to production deployment, rather than infrastructure in isolation.
- Pragmatic and standards-driven, with a bias toward reusable platform patterns over one-off solutions.
Nice to Have
- Experience securing managed and self-hosted AI platforms, including ChatUI integrations, MCP servers, and backend services.
- Familiarity with Apache Kafka and event-driven architectures.
- Hands-on experience with Snowflake integrations.
- Experience with ETL-as-Code frameworks such as dbt.
- Hands-on experience with workflow orchestrators such as Prefect or equivalent (e.g., Airflow, Dagster).
- Proven experience with AWS IAM and account management.
- Familiarity with ML frameworks such as PyTorch, Hugging Face, or scikit-learn.
What This Role Is Not:
- This is not a pure DevOps or SRE role. You will work directly with ML systems, training pipelines, and model deployment - not just maintain cloud infrastructure.
- This is not a data engineering role. While you will build and operate data pipelines, the focus is enabling ML training and inference workflows, not analytics or business reporting.
