Machine Learning Ops Engineer

Job Summary

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

pharma environment.

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

reliability, and compliance of AI systems handling sensitive data.

Key Responsibilities

1. Model & AI Agent Deployment

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

pipelines.

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

environments.

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.

Technical Skills & Competencies

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

Experience managing vector databases

Knowledge of monitoring tools (Prometheus, Grafana, ELK, Datadog, etc.)

Understanding of data security, encryption, compliance frameworks (GxP, HIPAA preferred)

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)