Machine Learning Ops. Engineer

NMS Consultant; Mumbai; On-site; INR 1,800,000 - 2,500,000; Full time

Benefits offered by this job

Pharma AI exposure

Collaborative culture

On-site gym

Job summary

NMS Consultant is seeking an MLOps Engineer to drive deployment, monitoring, and governance of ML models and AI agents in a regulated environment. You will build robust CI/CD pipelines, optimize GPU workloads, and manage hybrid cloud/on-prem infrastructure for scalable, secure AI solutions.

You will collaborate across teams to ensure reliability, compliance, and efficient model lifecycle management in production settings.

Qualifications

• BE/CS/AI or MCA with relevant certifications.
• 3-4 years of MLOps/DevOps experience in ML systems.
• Experience in pharma or regulated industries preferred.
• Strong Python and scripting skills.
• Hands-on CI/CD with Jenkins, GitHub Actions, GitLab CI or Azure DevOps.
• Experience with Docker, Kubernetes, Helm.
• Knowledge of ML lifecycle tools (MLflow, Kubeflow, Airflow).
• Experience with GPU optimization and distributed training.
• Familiarity with AWS/Azure/GCP and hybrid infra.
• Knowledge of monitoring tools (Prometheus, Grafana, ELK, Datadog).
• Understanding of data security, encryption, compliance (GxP, HIPAA).

Responsibilities

• Own end-to-end deployment of ML models and AI agents into production environments.
• Manage model versioning, rollback strategies, and lifecycle management.
• Ensure high availability, scalability, and reliability of deployed systems.
• Design and implement CI/CD pipelines for ML/GenAI workflows.
• Automate training, testing, validation, and deployment processes.
• Integrate testing, performance checks, and compliance gates.
• Maintain GPU-optimized infrastructure and monitor compute utilization.
• Support containerized deployments using Docker and Kubernetes.
• Implement observability with monitoring and alerting for ML systems.
• Deploy and manage vector databases (FAISS, Pinecone, Weaviate, Chroma).
• Ensure data security, access control, and regulatory compliance.

Skills

Python

CI/CD tools

Docker

Kubernetes

GPU optimization

Monitoring tools

Security & compliance

Regulated industry experience

Education

BE in Data Science / AI / CS or MCA

Certification in CI/CD or ML DevOps

Tools

Jenkins

GitHub Actions

GitLab CI

Azure DevOps

MLflow

Kubeflow

Airflow

Docker

Job description

We are seeking a skilled and detail-oriented MLOps Engineer with 3-4 years of experience to drive

the deployment, monitoring, and operational excellence of AI agents and machine learning models

within enterprise environments. The ideal candidate will be responsible for building robust CI/CD

pipelines for AI systems, optimizing GPU workloads, managing hybrid infrastructure (on-premises

and cloud), and ensuring performance, security, and compliance particularly within a regulated

This role requires strong experience in productionizing ML/GenAI solutions and ensuring scalability,

reliability, and compliance of AI systems handling sensitive data.

Key Responsibilities

• Own end-to-end deployment of ML models and AI agents into production environments.
• Manage model versioning, rollback strategies, and lifecycle management.
• Ensure high availability, scalability, and reliability of deployed systems.

2. CI/CD for AI Systems

• Design and implement CI/CD pipelines tailored for ML and GenAI workflows.
• Automate model training, testing, validation, and deployment processes.
• Integrate model testing frameworks, performance checks, and compliance gates into
• Enable seamless integration between development, staging, and production environments.

3. Infrastructure & GPU Optimization

• Manage and optimize GPU-based workloads for model training and inference.
• Monitor and improve compute utilization, cost efficiency, and latency.
• Administer and maintain cloud (AWS/Azure/GCP) and/or on-prem infrastructure
• Support containerized deployments using Docker and Kubernetes.

4. Performance Monitoring & Observability

• Implement infrastructure and model performance monitoring systems.
• Track system health, latency, throughput, resource utilization, and failure rates.
• Establish alerting, logging, and incident response processes.
• Continuously improve system performance and reliability through proactive monitoring.

5. Vector Database & Data Infrastructure Management

• Deploy and manage vector databases (e.g., FAISS, Pinecone, Weaviate, Chroma).
• Optimize indexing, embedding pipelines, and retrieval performance for GenAI applications.
• Ensure high availability and backup strategies for AI data systems.

6. Security, Access Control & Compliance

• Implement role-based access control (RBAC) and secure authentication mechanisms.
• Ensure infrastructure and AI systems comply with pharma regulatory standards and internal

governance policies.

• Manage sensitive data securely, including encryption (at rest and in transit).
• Support audit readiness and documentation for compliance reviews.

Qualifications

• BE in Data Science / Artificial Intelligence / Computer Science or MCA
• Certification in CI/CD, Machine Learning, or related DevOps technologies
• 3-4 years of experience in MLOps / DevOps for ML systems
• Experience in the pharmaceutical or regulated industry (preferred)
• Strong programming skills in Python and scripting languages
• Hands-on experience with CI/CD tools (Jenkins, GitHub Actions, GitLab CI, Azure DevOps, etc.)
• Experience with Docker, Kubernetes, Helm
• Knowledge of ML lifecycle tools (MLflow, Kubeflow, Airflow, etc.)
• Experience with GPU optimization and distributed training frameworks
• Familiarity with cloud platforms (AWS/Azure/GCP) and hybrid infrastructure
• Knowledge of monitoring tools (Prometheus, Grafana, ELK, Datadog, etc.)
• Understanding of data security, encryption, compliance frameworks (GxP, HIPAA preferred)

Preferred Attributes

• Strong problem-solving and troubleshooting skills
• Ability to work independently and manage production-critical systems
• Detail-oriented with strong documentation practices
• Experience supporting enterprise GenAI applications
• Good communication and cross-functional collaboration skills

What We Offer

• Exposure to regulated pharma AI environments
• Collaborative and innovation-driven culture
• Free access to on-site gym facility to support employee wellness