Machine Learning Ops. Engineer

Key Responsibilities

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.

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.

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.

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.

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.

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)