MLOps Engineer

Meril; Chennai District; On-site; INR 1,500,000 - 2,100,000; Full time

Job summary

Meril is seeking an experienced MLOps Engineer focused on healthcare AI to design, implement, and maintain scalable ML pipelines for deploying deep learning models and LLMs in clinical research and diagnostics.

You will work with data scientists and clinicians to integrate models into healthcare workflows, ensure compliance, and optimize inference for production. 3+ years of hands-on MLOps experience is required.

Qualifications

• Bachelor’s or master’s degree in computer science, engineering, or a related field.
• Minimum 3+ years experience as an MLOps Engineer or similar role.
• Hands-on experience deploying deep vision models (e.g., ResNet, U‑Net) and LLMs (GPT, BERT) in healthcare contexts.
• Experience with healthcare AI, medical imaging data (DICOM), or clinical datasets.
• Proficiency in Python, Docker, Kubernetes, and CI/CD pipelines.
• Familiarity with TensorFlow, PyTorch, Kubeflow, SageMaker, Vertex AI.
• Understanding of cloud platforms and version control (Git); awareness of HIPAA/GDPR compliance.

Responsibilities

• Design CI/CD pipelines for ML models using Jenkins, GitHub Actions, or GitLab CI.
• Deploy deep learning models and LLMs in production with Docker, Kubernetes, and cloud platforms.
• Develop monitoring and alerting for ML models with Prometheus, Grafana, or MLflow.
• Collaborate with data scientists and clinicians to integrate models into healthcare workflows.
• Implement model and data versioning with DVC, MLflow, or Kubeflow.
• Ensure healthcare regulatory compliance, data security, auditing, and privacy.
• Optimize inference through quantization and serving with TensorFlow Serving, TorchServe, or BentoML.
• Troubleshoot deployments and perform A/B testing for iterations.
• Automate infra as code using Terraform or Ansible.

Skills

MLOps

Python

Docker

Kubernetes

CI/CD

TensorFlow

PyTorch

Git

Regulatory compliance

DICOM experience

Education

Bachelor's or Master's in CS/Engineering

Tools

Kubeflow

SageMaker

Vertex AI

TensorFlow

PyTorch

BentoML

Terraform

Ansible

Job description

Shift Timings: Monday to Saturday (9 AM to 5.30 PM).

Experience: 3+ Years.

About the Role:

We are seeking an experienced MLOps Engineer with a focus on healthcare AI to join our innovative team. This role involves designing, implementing, and maintaining scalable MLOps pipelines for deploying deep learning models, including very deep vision models and large language models (LLMs), in clinical research, diagnostics, and healthcare analytics. The ideal candidate will have hands‑on experience in healthcare AI environments, ensuring reliable, efficient, and compliant model deployments.

Key Responsibility:

• Design and implement CI/CD pipelines for machine learning models using tools like Jenkins, GitHub Actions, or GitLab CI.
• Deploy and manage very deep vision models (e.g., CNNs for medical imaging) and LLMs (e.g., for NLP in clinical notes) in production environments using Docker, Kubernetes, and cloud platforms (e.g., AWS, Azure, GCP).
• Develop monitoring, logging, and alerting systems for ML models to ensure performance, drift detection, and retraining triggers using tools like Prometheus, Grafana, or MLflow.
• Collaborate with data scientists, AI engineers, and clinicians to integrate models into healthcare workflows, handling large-scale medical datasets.
• Implement version control for models and data using tools like DVC, MLflow, or Kubeflow.
• Ensure compliance with healthcare regulations (e.g., HIPAA, GDPR) through secure data handling, model auditing, and privacy-preserving techniques.
• Optimize model inference for efficiency, including model quantization, serving with TensorFlow Serving, TorchServe, or BentoML.
• Troubleshoot deployment issues and perform A/B testing for model iterations.
• Automate infrastructure as code using Terraform or Ansible for reproducible environments.

Must-Have Skills & Experience

• Bachelor’s or master’s degree in computer science, Engineering, or a related field.
• Minimum 3 years of experience as an MLOps Engineer or in a similar role.
• Hands‑on experience deploying very deep vision models (e.g., ResNet, U-Net for medical imaging) and LLMs (e.g., GPT variants, BERT for healthcare NLP).
• Some experience in healthcare AI, such as working with medical imaging data (DICOM) or clinical datasets.
• Proficiency in Python, containerization (Docker), orchestration (Kubernetes), and CI/CD.
• Familiarity with ML frameworks like TensorFlow, PyTorch, and MLOps platforms like Kubeflow, SageMaker, or Vertex AI.
• Strong understanding of cloud computing, DevOps practices, and version control (Git).
• Knowledge of data security and regulatory compliance in healthcare.