MLOps Engineer

2 years

0 Lacs

Posted:1 day ago| Platform: Linkedin logo

Apply

Work Mode

On-site

Job Type

Full Time

Job Description

Company Description

EXL Health combines deep domain expertise with analytic insights and technology-enabled services to transform how care is delivered, managed, and paid. We collaborate with our clients to solve complex problems and enhance their performance with scalable solutions aided by Human Ingenuity. With data on more than 260 million lives, we work with hundreds of organizations across the healthcare ecosystem, including payers, PBMs, and provider organizations. Our goal is to improve care quality, manage risk, optimize revenue, and reduce administrative waste.


Experience: 2+Years of experience

Work Mode: Hybrid

Notice Period: 30 - 45 days


1. ML Pipeline & Automation

  • Design, build, and maintain

    end-to-end ML pipelines

    for model training, testing, and deployment.
  • Implement

    automated workflows

    for data preprocessing, model validation, and performance tracking.
  • Use orchestration tools like

    Kubeflow, MLflow, Airflow, or Prefect

    to streamline experiments and model lifecycle.

2. Model Deployment & Serving

  • Containerize ML models using

    Docker

    and deploy them via

    Kubernetes

    ,

    AWS SageMaker

    ,

    Azure ML

    , or

    GCP Vertex AI

    .
  • Develop APIs and services in

    Python

    (Flask, FastAPI, or Django) for scalable model serving.
  • Integrate

    CI/CD pipelines

    for model deployment with GitHub Actions, Jenkins, or GitLab CI.

3. DevOps & Infrastructure

  • Implement

    infrastructure as code (IaC)

    using

    Terraform, Ansible, or CloudFormation

    .
  • Manage

    cloud infrastructure

    (AWS, Azure, or GCP) for ML workloads and optimize for cost and performance.
  • Set up

    monitoring, logging, and alerting

    for production ML models using tools like

    Prometheus, Grafana, ELK, or Datadog

    .
  • Ensure

    security, scalability, and reliability

    across ML systems.

4. Collaboration & Best Practices

  • Work with

    data scientists

    to translate experimental notebooks into production-grade code.
  • Manage model versioning, experiment tracking, and data lineage with tools like

    DVC, MLflow, or Weights & Biases

    .
  • Promote

    DevOps culture

    in ML teams through automation, reproducibility, and CI/CD.
  • Document and standardize MLOps practices across teams.

Required Skills & Qualifications

  • Strong programming skills in Python

    — particularly for data pipelines, APIs, and automation.
  • Solid understanding of

    DevOps tools and practices

    , including CI/CD, containers, and orchestration.
  • Experience with

    ML lifecycle management

    (training, deployment, monitoring, retraining).
  • Hands-on experience with

    cloud platforms

    (AWS / GCP / Azure).
  • Familiarity with

    ML frameworks

    (TensorFlow, PyTorch, Scikit-learn).
  • Knowledge of

    version control systems (Git)

    and

    collaboration workflows

    .
  • Basic understanding of

    data engineering concepts

    (ETL, data validation, feature stores).

Mock Interview

Practice Video Interview with JobPe AI

Start Django Interview
cta

Start Your Job Search Today

Browse through a variety of job opportunities tailored to your skills and preferences. Filter by location, experience, salary, and more to find your perfect fit.

Job Application AI Bot

Job Application AI Bot

Apply to 20+ Portals in one click

Download Now

Download the Mobile App

Instantly access job listings, apply easily, and track applications.

coding practice

Enhance Your Skills

Practice coding challenges to boost your skills

Start Practicing Now

RecommendedJobs for You

noida, uttar pradesh, india

vapi, gujarat, india

hyderabad, pune, bengaluru