Machine Learning Operations Specialist

About Company :

They balance innovation with an open, friendly culture and the backing of a long-established parent company, known for its ethical reputation. We guide customers from what’s now to what’s next by unlocking the value of their data and applications to solve their digital challenges, achieving outcomes that benefit both business and society.

About Client:

Our client is a global digital solutions and technology consulting company headquartered in Mumbai, India. The company generates annual revenue of over $4.29 billion (₹35,517 crore), reflecting a 4.4% year-over-year growth in USD terms. It has a workforce of around 86,000 professionals operating in more than 40 countries and serves a global client base of over 700 organizations.

Our client operates across several major industry sectors, including Banking, Financial Services & Insurance (BFSI), Technology, Media & Telecommunications (TMT), Healthcare & Life Sciences, and Manufacturing & Consumer. In the past year, the company achieved a net profit of $553.4 million (₹4,584.6 crore), marking a 1.4% increase from the previous year. It also recorded a strong order inflow of $5.6 billion, up 15.7% year-over-year, highlighting growing demand across its service lines.

Key focus areas include Digital Transformation, Enterprise AI, Data & Analytics, and Product Engineering—reflecting its strategic commitment to driving innovation and value for clients across industries.

Job Title:

Location:

Type:

About ValueMomentum – Advanced Analytics Center

Advanced Analytics Center

dynamic, collaborative, and innovation-driven

Job Summary

Sr. MLOps Specialist

Key Responsibilities

1. Design & Implement MLOps Pipelines

• Build and maintain robust CI/CD pipelines for ML using

  Amazon SageMaker Pipelines, CodePipeline, Step Functions

  .
• Automate model

  training, evaluation, deployment, and monitoring

  .

2. Infrastructure & Cloud Management

• Use

  Infrastructure-as-Code (IaC)

  tools (CloudFormation, Terraform, CDK) for reproducible environments.
• Architect scalable ML infrastructure with

  AWS services

  (S3, Lambda, ECR, EC2, SageMaker).

3. Monitoring, Logging & Observability

• Implement

  model and data monitoring

  with SageMaker Model Monitor, CloudWatch, or third-party tools.
• Set up

  logging, alerts, and dashboards

  for model health and performance.

4. Governance & Compliance

• Manage

  model registries, lineage tracking, and audit logging

  .
• Ensure

  version control and approval workflows

  for ML assets.

5. Collaboration & Enablement

• Partner with

  data scientists, ML engineers, and DevOps teams

  to integrate ML workflows.
• Educate and mentor teams on

  MLOps best practices

  and AWS ML tooling.

Required Skills & Qualifications

• 8+ years

  experience in

  DataOps, DevOps, or ML Engineering

  with focus on

  cloud-based ML pipelines

  .
• Strong expertise with

  AWS services

  , including:
• Amazon SageMaker (training, deployment, Pipelines, Model Monitor)
• S3, Lambda, Step Functions, CodePipeline, ECR, CloudWatch
• Proficiency in

  Python, Bash, scripting

  for automation.
• Hands-on with

  CI/CD tools

  (Jenkins, GitHub Actions, CodeBuild).
• Experience with

  Docker

  and container orchestration (ECS, EKS optional).
• Solid understanding of

  ML lifecycle

  (feature engineering, training, deployment, monitoring).
• Familiarity with

  model tracking tools

  (MLflow, DVC, SageMaker Model Registry).
• Strong

  communication and collaboration

  skills.