Alignity

Machine Learning Operations (MLOps) Engineer

Alignity  •  Republic of India (Hybrid)  •  3 months ago
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Job Description


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.

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)


Benefits


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
Alignity

About Alignity

Successful companies gain back 30% of their budget & time each year. How?

They partner with Alignity to solve their challenges in

- Digital Transformation

- Employer Branding & Hiring

- Performance Innovation

See others share their specific benefits by partnering with us:

Clients: https://alignity.io/talent-acquisition/#WhyClientsTrustUs

Employees: https://alignity.io/candidate-services/#WhyEmployeesLoveUs

Connect with us if you are looking for Outsourcing, Staffing solutions in below niches

- Cloud/Data

- Cybersecurity

- AI/ML

- Fullstack

- Agile

- SAP

Industry
IT & Software
Company Size
11-50 employees
Headquarters
Plano, Texas
Year Founded
2008
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