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 Description

Job Title: Sr. MLOps Specialist – AWS Cloud

Location: Hyderabad

Type: Contractor

About ValueMomentum – Advanced Analytics Center

ValueMomentum has strategically established an Advanced Analytics Center to strengthen AI and analytics capabilities in the Property & Casualty (P&C) Insurance sector. This unit focuses on revolutionizing insurance with innovative AI solutions, fostering reusability, experimentation, and capability development.

Our environment is dynamic, collaborative, and innovation-driven, comprising data scientists, analysts, and consultants. The Advanced Analytics team delivers cutting-edge AI/analytics solutions across the P&C value chain – Product/Underwriting, Claims, Distribution, and Enterprise IT.

Job Summary

We are seeking an experienced Sr. MLOps Specialist with deep expertise in AWS services and machine learning deployment best practices. The role involves designing, building, and maintaining scalable, secure, and automated ML pipelines. You will bridge the gap between data science and engineering teams, ensuring ML models are production-ready and efficiently managed throughout their lifecycle.

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.

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