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)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