Staff MLOps Engineer

GoTo Meeting; Bengaluru; On-site; INR 2,000,000 - 3,000,000; Full time

Benefits offered by this job

Sodexo Meal Coupon

Internet Reimbursement

Mobile Reimbursement

Gym Reimbursement

Health Insurance

Transport Facility

Job summary

GoTo Meeting is seeking a Staff MLOps Engineer in Bengaluru, India. This role involves leading the design and implementation of MLOps platforms on AWS, overseeing the deployment and monitoring of machine learning models. The successful candidate will have 11-13 years of experience, including 5+ years specifically in MLOps or ML infrastructure roles. The position offers various perks, including meal coupons and health insurance.

Qualifications

• 11–13 years of overall experience, 5+ years in MLOps or ML roles.
• Strong experience deploying machine learning models on AWS.
• Proficiency in Python with ML frameworks.
• Hands-on experience with Docker and Kubernetes.

Responsibilities

• Architect scalable MLOps platforms on AWS.
• Design and maintain CI/CD pipelines.
• Implement monitoring for model performance.
• Lead design using AWS services.

Skills

MLOps

AWS

Python

Docker

Kubernetes

TensorFlow

PyTorch

Education

Bachelor's degree

Tools

AWS SageMaker

GitHub Actions

Terraform

Job description

Staff MLOps Engineer

Overview

We are looking for a seasoned Staff MLOps Engineer to lead the design, implementation, and scaling of enterprise-grade machine learning platforms on AWS. This role will focus on building reliable, secure, and cost-efficient MLOps systems that enable data scientists and engineers to deploy, monitor, and manage ML models in production. As a Staff Engineer, you will provide technical leadership, define best practices, and drive cross-team alignment on ML platform architecture.

Duties & Responsibilities

MLOps Platform & Architecture

• Architect and own scalable MLOps platforms on AWS supporting model training, deployment, monitoring, and governance.
• Design and maintain end-to-end ML CI/CD pipelines, including data validation, model training, testing, approval, and deployment.
• Establish standards for model lifecycle management, experiment tracking, versioning, reproducibility, and rollback.

Model Deployment & Monitoring

• Enable real‑time, batch, and asynchronous model inference using AWS-native and container‑based solutions.
• Implement monitoring for model performance, data drift, concept drift, and operational metrics.
• Ensure high availability, fault tolerance, and observability for production ML systems.

AWS Cloud & Infrastructure

• Lead design and implementation using AWS services, including but not limited to:
• Amazon SageMaker (training, hosting, pipelines, feature store)
• EKS, ECS, EC2, Lambda for model serving and orchestration
• S3, Glue, Athena, Redshift for data storage and analytics
• CloudWatch, X‑Ray for logging and monitoring
• Implement Infrastructure as Code (IaC) using Terraform or AWS CloudFormation.
• Optimize ML workloads for cost, performance, and scalability, including GPU/spot instance strategies.

DevOps, Security & Compliance

• Build and maintain CI/CD pipelines using tools such as GitHub Actions, GitLab CI, Jenkins, or AWS CodePipeline.
• Enforce security best practices (IAM, VPC, encryption, secrets management).
• Support compliance, auditability, and governance requirements for ML systems.

Technical Leadership & Collaboration

• Serve as a Staff‑level technical leader, influencing MLOps architecture across multiple teams.
• Mentor engineers and data scientists on production ML best practices.
• Partner with Data Science, Data Engineering, Platform, and Product teams to align ML solutions with business goals.
• Contribute to the long‑term ML platform roadmap and strategy.

Skills Required

Mandatory Skills Required:

• 11–13 years of overall experience, with 5+ years in MLOps, ML Platform, or ML Infrastructure roles.
• Strong experience deploying and operating machine learning models in production on AWS.
• Proficiency in Python and experience with ML frameworks such as TensorFlow, PyTorch, Scikit‑learn.
• Deep hands‑on experience with Docker and Kubernetes (EKS).
• Strong understanding of Amazon SageMaker and its ecosystem.
• Experience with CI/CD systems and Git‑based workflows.
• Solid background in distributed systems, system design, and cloud architecture.

Preferred / Nice‑to‑Have Skills:

• Experience with SageMaker Feature Store, Pipelines, Model Registry, or MLflow.
• Exposure to LLMOps / GenAI on AWS (Bedrock, custom LLM deployment, vector databases like OpenSearch, Pinecone).
• Experience with streaming and real‑time pipelines (Kafka, Kinesis, Spark).
• Experience in regulated or high‑scale environments (finance, healthcare, retail, etc.).
• AWS certifications (Solutions Architect, Machine Learning Specialty) are a plus.

Soft Skills:

• Strong ownership and decision‑making ability at a Staff level.
• Excellent communication skills across engineering, data science, and leadership teams.
• Ability to balance short‑term delivery with long‑term platform vision.
• Passion for building reliable, scalable, and maintainable ML systems.

Qualifications Required:

• Bachelor’s degree (B.A.) from four‑year college or university, or equivalent combination of education and experience.
• 11–13 years of overall experience, with 5+ years in MLOps, ML Platform, or ML Infrastructure roles.

Perks & Benefits

• Sodexo Meal Coupon
• Internet Reimbursement
• Mobile Reimbursement
• Gym Reimbursement
• Health Insurance - Personal, Term Life Policy, Dependents, Dental Cover, OPD Cover & etc
• Transport Facility - Subsidized Rate