Do you love a career where you Experience, Grow & Contribute at the same time, while earning at least 10% above the market? If so, we are excited to have bumped onto you.

Learn how we are redefining the meaning of work, and be a part of the team raved by Clients, Job-seekers and Employees.

- Jobseeker Video Testimonials

- Employee Glassdoor Reviews

If you are a Machine Learning Operations (MLOps) Engineer looking for excitement, challenge and stability in your work, then you would be glad to come across this page.

We are an IT Solutions Integrator/Consulting Firm helping our clients hire the right professional for an exciting long-term project. Here are a few details.

Check if you are up for maximizing your earning/growth potential, leveraging our Disruptive Talent Solution.

Role:Machine Learning Operations (MLOps) Engineer

Location: Hyderabad | Bengaluru | Chennai | Pune | Mumbai | Kolkata | Gurgaon

Work Mode: Hybrid

Relevent Experience: 6-9 Years

Type: Contract to Hire

Requirements

Key Responsibilities

ML CI/CD & Deployment

- Design, build, and maintain CI/CD pipelines for Machine Learning workflows, including:

- Model training

- Model validation

- Model packaging

- Model deployment

- Ensure ML pipelines operate efficiently across development, testing, and production environments.

Model Deployment & Serving

- Implement and manage model deployment patterns, including:

- Batch inference

- Real-time inference

- Streaming inference

- Develop and maintain model serving infrastructure for scalable and reliable ML inference.

Model Observability & Monitoring

- Establish comprehensive model observability frameworks to monitor:

- Data drift

- Model performance degradation

- Latency





- System failures

- Bias and quality signals

Feature Engineering Infrastructure

- Build and manage feature pipelines and feature stores.

- Ensure data lineage, reproducibility, and traceability across ML workflows.

Experiment Management & Model Governance

- Operationalize experiment tracking frameworks.

- Manage model registry and artifact management systems, including:

- Versioning of code

- Versioning of datasets

- Versioning of models

Model Testing & Validation

- Define and automate testing frameworks for ML systems, including:

- Unit testing

- Integration testing

- Implement validation gates and model promotion criteria before deployment to production.

Security & Compliance

- Collaborate with security and compliance teams to implement:

- Access controls

- Secrets management

- Audit logging

- Risk management controls

Performance Optimization

- Optimize infrastructure for training and inference workloads, including:

- Autoscaling

- Resource right-sizing

- GPU utilization

- Workload scheduling

- Ensure efficient compute utilization and cost optimization.

Operational Excellence

- Develop and maintain:

- Operational runbooks

- SLAs (Service Level Agreements)

- SLOs (Service Level Objectives)

- Incident response processes

- Operational monitoring dashboards





Architecture & Platform Standards

- Contribute to reference architectures for machine learning platforms.

- Develop engineering standards, reusable templates, and best practices for ML product teams.

Required Skills & Expertise

- Strong experience in Machine Learning Operations (MLOps) and ML platform engineering

- Expertise in CI/CD pipelines for ML workflows

- Experience managing ML model deployment patterns (batch, real-time, streaming)

- Knowledge of model observability and monitoring

- Hands-on experience with feature pipelines and feature stores

- Experience implementing experiment tracking, model registry, and artifact management

- Familiarity with model testing frameworks (unit and integration testing)

- Strong understanding of ML governance, security, and compliance practices

- Experience with autoscaling infrastructure, GPU utilization, and workload scheduling

- Ability to build operational dashboards and incident management processes

- Strong experience designing ML reference architectures and reusable engineering templates

Key Focus Areas

- ML CI/CD pipelines

- Model deployment and serving infrastructure

- Model monitoring and observability

- Feature store management

- Experiment tracking and artifact management

- Testing automation for ML systems

- Security, compliance, and governance

- Cost optimization and GPU utilization

- Operational reliability (SLA/SLO/Incident management)

Perks

Visit us at http://alignity.io/careers. Alignity Solutions is an Equal Opportunity Employer, M/F/V/D.

CEO Message: Click Here

Clients Testimonial: Click Here

📌 Machine Learning Operations (MLOps) Engineer (Serilingampalle (M))
🏢 Alignity Solutions
📍 Serilingampalle (M)