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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)

- Solid 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)

Benefits

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

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📌 Machine Learning Operations (MLOps) Engineer (Serilingampalle (M))
🏢 Important Business
📍 Serilingampalle (M)