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Machine Learning Operations Engineer Job Opening In India, India – Now Hiring HCLTech


Job description

HCLTech is hiring MLOps Engineer


Job Title:

ML Ops Engineer / ML Engineer

Job Overview:

We are looking for an experienced MLOps Engineer to help deploy, scale, and manage machine learning models in production environments.

You will work closely with data scientists and engineering teams to automate the machine learning lifecycle, optimize model performance, and ensure smooth integration with data pipelines.

Overall Experience: 5 to 10 yrs

Notice Period: Immediate/30 days

Location: Bangalore/Chennai/Noida/Hyderabad


Key Responsibilities:

Transform prototypes into production-grade models

  • Assist in building and maintaining machine learning pipelines and infrastructure across cloud platforms such as AWS, Azure, and GCP.

  • Develop REST APIs or FastAPI services for model serving, enabling real-time predictions and integration with other applications.

  • Collaborate with data scientists to design and develop drift detection and accuracy measurements for live models deployed.

  • Collaborate with data governance and technical teams to ensure compliance with engineering standards.

Maintain models in production

  • Collaborate with data scientists and engineers to deploy, monitor, update, and manage models in production.

  • Manage the full CI/CD cycle for live models, including testing and deployment.

  • Develop logging, alerting, and mitigation strategies for handling model errors and optimize performance.

  • Troubleshoot and resolve issues related to ML model deployment and performance.

  • Support both batch and real-time integrations for model inference, ensuring models are accessible through APIs or scheduled batch jobs, depending on use case.

Contribute to AI platform and engineering practices

  • Contribute to the development and maintenance of the AI infrastructure, ensuring the models are scalable, secure, and optimized for performance.

  • Collaborate with the team to establish best practices for model deployment, version control, monitoring, and continuous integration/continuous deployment (CI/CD).

  • Drive the adoption of modern AI/ML engineering practices and help enhance the team’s MLOps capabilities.

  • Develop and maintain Flask or FastAPI-based microservices for serving models and managing model APIs.

Minimum Required Skills:


5+ yrs of experience in below mentioned skills:

  • Bachelor's degree in computer science, analytics, mathematics, statistics.

  • Strong experience in Python, SQL, Pyspark.

  • Solid understanding and knowledge of containerization technologies (Docker, Podman, Kubernetes).

  • Proficient in CI/CD pipelines, model monitoring, and MLOps platforms (e.G., AWS SageMaker, Azure ML, MLFlow).

  • Proficiency in cloud platforms, specifically AWS, Azure and GCP.

  • Familiarity with ML frameworks such as TensorFlow, PyTorch, Scikit-learn.

  • Familiarity with batch processing integration for large-scale data pipelines.

  • Experience with serving models using FastAPI, Flask, or similar frameworks for real-time inference.

  • Certifications in AWS, Azure or ML technologies are a plus.

  • Experience with Databricks is highly valued.

  • Strong problem-solving and analytical skills.

  • Ability to work in a team-oriented, collaborative environment.

Tools and Technologies:

Model Development & Tracking: TensorFlow, PyTorch, scikit-learn, MLflow, Weights & Biases

Model Packaging & Serving: Docker, Kubernetes, FastAPI, Flask, ONNX, TorchScript

CI/CD & Pipelines: GitHub Actions, GitLab CI, Jenkins, ZenML, Kubeflow Pipelines, Metaflow

Infrastructure & Orchestration: Terraform, Ansible, Apache Airflow, Prefect

Cloud & Deployment: AWS, GCP, Azure, Serverless (Lambda, Cloud Functions)

Monitoring & Logging: Prometheus, Grafana, ELK Stack, WhyLabs, Evidently AI, Arize

Testing & Validation: Pytest, unittest, Pydantic, Great Expectations

Feature Store & Data Handling: Feast, Tecton, Hopsworks, Pandas, Spark, Dask

Message Brokers & Data Streams: Kafka, Redis Streams

Vector DB & LLM Integrations (optional): Pinecone, FAISS, Weaviate, LangChain, LlamaIndex, PromptLayer

Required Skill Profession

Computer Occupations


  • Job Details

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Unlock Your Machine Learning Potential: Insight & Career Growth Guide


Real-time Machine Learning Jobs Trends (Graphical Representation)

Explore profound insights with Expertini's real-time, in-depth analysis, showcased through the graph here. Uncover the dynamic job market trends for Machine Learning in India, India, highlighting market share and opportunities for professionals in Machine Learning roles.

18638 Jobs in India
18638
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Are You Looking for Machine Learning Operations Engineer Job?

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The Work Culture

An organization's rules and standards set how people should be treated in the office and how different situations should be handled. The work culture at HCLTech adheres to the cultural norms as outlined by Expertini.

The fundamental ethical values are:

1. Independence

2. Loyalty

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Interview Tips for Machine Learning Operations Engineer Job Success

HCLTech interview tips for Machine Learning Operations Engineer

Here are some tips to help you prepare for and ace your Machine Learning Operations Engineer job interview:

Before the Interview:

Research: Learn about the HCLTech's mission, values, products, and the specific job requirements and get further information about

Other Openings

Practice: Prepare answers to common interview questions and rehearse using the STAR method (Situation, Task, Action, Result) to showcase your skills and experiences.

Dress Professionally: Choose attire appropriate for the company culture.

Prepare Questions: Show your interest by having thoughtful questions for the interviewer.

Plan Your Commute: Allow ample time to arrive on time and avoid feeling rushed.

During the Interview:

Be Punctual: Arrive on time to demonstrate professionalism and respect.

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Answer Thoughtfully: Listen carefully, take a moment to formulate clear and concise responses. Highlight relevant skills and experiences using the STAR method.

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To prepare for your Machine Learning Operations Engineer interview at HCLTech, research the company, understand the job requirements, and practice common interview questions.

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By following these tips, you can increase your chances of making a positive impression and landing the job!

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