MLOPS

Location: Bengaluru

Hi,
I hope you are doing well.
I am reaching out regarding an opportunity for the position of  [MLOPS]  with  [HCL TECH] . Based on your background and experience, we believe you could be a strong potential fit for this role.
Position Details:

Bangalore/Pune/Hyderabad/Noida/Chennai

Job Description:

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

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:

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 Sage Maker, Azure ML, MLFlow).
Proficiency in cloud platforms, specifically AWS, Azure and GCP.
Familiarity with ML frameworks such as Tensor Flow, 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:  Tensor Flow, PyTorch, scikit-learn, MLflow, Weights & Biases
Model Packaging & Serving:  Docker, Kubernetes, FastAPI, Flask, ONNX, Torch Script
CI/CD & Pipelines:  Git Hub Actions, Git Lab 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, Why Labs, 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, Lang Chain, Llama Index, Prompt Layer

CTC / Salary Details
CTC Range:  [Insert exact CTC or range]

Location:

[Work location / Hybrid]

Employment Type:

[Full‑time]
We would love to know if you are open to exploring this opportunity.
If interested, please reply with the following details:
Updated resume
Current CTC
Expected CTC
Notice period
Current location
Mail your resume to:
If this role isn’t the right fit for you at this time, we would appreciate it if you could refer anyone in your network who might be interested.
Looking forward to hearing from you!
Warm regards,
Sayani Sur