Senior Machine Learning Operations Engineer

Siemens Mobility; Delhi; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Brightly Software India Private Limited is seeking a Senior MLOps Engineer to architect, develop, and operate end‑to‑end ML infrastructure on AWS. You will bridge ML engineering, cloud infrastructure, and developer productivity to productionize models and data pipelines.

You will implement IaC with Terraform, CI/CD with CodePipeline/GitHub Actions, and monitor systems with CloudWatch, Prometheus, and Grafana. Hybrid work model in India is offered.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
• 10+ years in ML engineering, DevOps, cloud engineering, or MLOps with senior/lead experience.
• 3+ years designing robust, scalable ML systems in cloud environments (AWS).
• 5+ years building on AWS ML ecosystem (SageMaker, S3, Lambda, ECR, IAM, VPC).
• 3+ years deploying ML models in production; strong Python & Docker experience.

Responsibilities

• Design, build, and operate ML/AI development platforms on AWS (SageMaker, S3, Lambda, ECS/EKS).
• Implement infrastructure‑as‑code (Terraform) and manage orchestration (Step Functions, Airflow).
• Develop data ingestion/transform pipelines with data quality tooling (Great Expectations, Deequ).
• Create CI/CD pipelines for ML (CodeBuild/CodePipeline/GitHub Actions) with testing and validation.
• Deploy real‑time and batch inference endpoints and optimize cost/performance.
• Ensure monitoring, observability, and governance (CloudWatch, Prometheus, Grafana).
• Collaborate with data scientists and engineers to productionize models and GenAI workloads.

Skills

Python

AWS

MLOps

CI/CD

Terraform

Docker

Kubernetes

SageMaker

Monitoring

Security

Education

Bachelor’s or Master’s degree in CS/Engineering

Tools

SageMaker

ECS/EKS

Airflow

GitHub Actions

CodePipeline

Job description

Senior Machine Learning Operations Engineer

Job ID

509409

Posted since

09-Jun-2026

Organization

Smart Infrastructure

Field of work

Research & Development

Company

Brightly Software India Private Limited

Experienced Professional

Job type

Full-time

Hybrid (Remote/Office)

Employment type

Permanent

Location(s)

About Brightly Software

Brightly Software is a leader in intelligent asset management and operational optimization, empowering organizations with data‑driven insights. As we expand our AI and ML capabilities, we are seeking a Senior MLOps Engineer to build and scale the infrastructure that powers our next generation of predictive and autonomous solutions.

Role Overview

As a Senior MLOps Engineer, you will architect, develop, and operate end‑to‑end machine learning infrastructure on AWS. You will work at the intersection of ML engineering, cloud infrastructure, and developer productivity—enabling Brightly's data science teams to move seamlessly from experimentation to reliable, secure, and cost‑efficient production systems.

Your work will ensure that ML models and data pipelines are scalable, observable, and compliant with best‑in‑class MLOps practices.

Key Responsibilities

• Design, build, and operate ML/AI development platforms on AWS, leveraging services such as Amazon SageMaker (Studio, Training, Real‑Time & Async Inference, Pipelines, Feature Store), S3, Glue, Lambda, ECS/EKS, and related cloud infrastructure.
• Implement infrastructure‑as‑code using Terraform or equivalent, and manage workflow orchestration using AWS Step Functions or Airflow.
• Build automated data ingestion and transformation pipelines using S3, Glue, EMR/Spark, and Redshift, incorporating data quality and lineage tooling (e.g., Great Expectations, Deequ).

CI/CD for Machine Learning

• Develop CI/CD pipelines for ML with CodeBuild, CodePipeline, or GitHub Actions, integrating unit tests, data contract checks, model validation, canary/shadow deployments, and automated rollback strategies.

Model Deployment & Operations

• Deploy real‑time inference endpoints (SageMaker endpoints or FastAPI‑based services on Lambda/ECS/EKS) and scalable batch processing jobs.
• Define SLOs, implement autoscaling, and drive cost/performance optimizations across ML workloads.

Monitoring, Observability & Governance

• Implement production monitoring for drift, bias, and performance using SageMaker Model Monitor and service telemetry tools like CloudWatch, Prometheus, and Grafana.
• Enforce security and governance best practices, including least‑privilege IAM, VPC‑isolated architectures, encryption, and secret management.

Cross‑Functional Collaboration

• Partner closely with data scientists, ML engineers, and backend engineers to productionize ML models and streamline development workflows.
• Contribute to the integration of emerging GenAI workloads, including Amazon Bedrock, vector databases (e.g., OpenSearch), and RAG pipelines.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• 10+ years of professional experience in ML engineering, DevOps, cloud engineering, or MLOps roles, with at least 3 years in a senior or lead capacity.
• 3+ years of proven track record in designing and architecting robust, scalable ML systems and infrastructure in cloud environments, particularly on AWS.
• 5+ years of deep experience building on the AWS ML ecosystem, including SageMaker, S3, Lambda, ECR, EKS/ECS, Step Functions, IAM, VPC networking, and CI/CD tooling.
• 3+ years of hands‑on experience deploying, maintaining, and scaling ML models in production environments.
• 3+ years of strong Python development skills and familiarity with Docker‑based workflows.
• 5+ years of solid understanding of ML lifecycles, model evaluation, and monitoring patterns.
• 5+ years of extensive experience with infrastructure‑as‑code (Terraform, CloudFormation).
• 5+ years of expertise in designing system architecture for ML platforms, including microservices, container orchestration, and cloud networking.
• 3+ years of familiarity with MLOps best practices as defined by AWS and industry standards.
• 2+ years of experience with data quality frameworks (Great Expectations, Deequ).
• 2+ years of experience optimizing distributed training workflows on AWS.
• 3+ years of knowledge of security and compliance requirements for ML in enterprise settings, such as IAM, encryption, and secret management.
• 2+ years of experience with monitoring tools (CloudWatch, Prometheus, Grafana) and implementing model observability solutions.
• 5+ years of effective cross‑functional collaboration skills, working closely with data scientists, ML engineers, and software engineers to deliver production‑grade ML solutions.
• 7+ years of excellent problem‑solving and communication abilities, with a focus on delivering scalable, reliable, and cost‑effective ML platforms.