ML 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