II Software Engineer AI​/ML Ops

As a Machine Learning Operations Engineer, you will play a pivotal role in bridging the gap between data science and production environments. You will collaborate with cross‑functional teams to streamline the machine learning lifecycle, ensuring seamless integration into operational systems.

• Leadership and Strategy
  • Partner with data science, security, and product teams to set evaluation and governance standards (guardrails, bias, drift, latency SLAs).
  • Mentor senior engineers and drive design reviews for ML pipelines, model registries, and agentic runtime environments.
  • Lead incident response and reliability strategies for ML/AI systems.
• AI System Deployment and Integration
  • Collaborate with development teams to integrate AI solutions into existing workflows and applications.
  • Ensure seamless integration with different platforms and technologies.
  • Define and manage MCP Registry onboarding, lifecycle versioning, and dependency governance.
  • Build CI/CD pipelines automating LLM agent deployment, policy validation, and prompt evaluation of workflows.
  • Develop and operationalize experimentation frameworks for agent evaluations, scenario regression, and performance analytics.
  • Implement logging, metering, and auditing for agent behavior, function calls, and compliance alignment.
  • Create scalable observability systems—tracking conversation outcomes, factual accuracy, latency, escalation patterns, and safety events.
  • Architect end‑to‑end guardrails for AI agents including prompt injection protection, identity‑aware routing, and tool usage authorization.
  • Collaborate cross‑functionally to standardize authentication, authorization, and session governance for multi‑agent runtimes.
• Model Deployment and Integration
  • Architect and standardize model registries and feature stores to support version tracking, lineage, and reproducibility across environments.
  • Lead the deployment of machine learning models into production environments, ensuring scalability, reliability, and efficiency.
  • Collaborate with software engineers to integrate machine learning models into existing applications and systems.
  • Implement and maintain APIs for model inference.
• Infrastructure and Environment Management
  • Design and manage training infrastructure including distributed training orchestration, GPU/TPU resource allocation, and automatic scaling.
  • Implement CI/CD for model workflows using pipelines integrated with model validation, bias checks, and rollback automation.
  • Build standardized experimentation frameworks for reproducible training, tuning, and deployment cycles (MLflow, W&B, Kubeflow).
  • Manage and optimize the infrastructure required for machine learning operations in cloud.
  • Work closely with other teams to ensure the availability, security, and performance of machine learning systems.
• Monitoring and Maintenance
  • Implement robust monitoring solutions for deployed machine learning models to detect issues and ensure performance.
  • Collaborate with data scientists and engineers to address and resolve model performance and data quality issues.
  • Conduct regular system maintenance, updates, and optimizations to ensure optimal performance of machine learning solutions.
• Automation and Orchestration
  • Develop and maintain automation scripts and tools for managing machine learning workflows.
  • Implement orchestration systems to streamline the end‑to‑end machine learning lifecycle, from data preparation to model deployment.
• Collaboration with Data Science Teams
  • Collaborate with data scientists to understand model requirements and constraints for deployment.
  • Facilitate the transition of machine learning models from research to production, ensuring scalability and efficiency.
• Performance Optimization
  • Identify and implement optimizations to enhance the performance and efficiency of machine learning models in production.
  • Conduct performance analysis and implement improvements based on resource utilization metrics.
• Security and Compliance
  • Implement security measures to protect machine learning systems and data.
  • Ensure compliance with regulatory requirements and industry standards related to machine learning and data privacy.
  • Integrate audit controls, metadata storage, and lineage…