Senior AI Engineer

Summary

Build and maintain an AI platform: design services, deploy LLMs, optimize inference, and implement MLOps/RAG workflows for enterprise AI solutions.

Design, develop, and maintain AI Hub platform modules, services, and shared AI capabilities.
Build reusable AI platform components that support multiple AI products and business solutions.
Develop APIs, SDKs, and platform services to accelerate AI application development.
Create standardized frameworks and platform capabilities for AI solution delivery.
Deploy, manage, and optimize Large Language Models (LLMs), Machine Learning models, embeddings, and AI services.
Manage model lifecycle including deployment, versioning, monitoring, scaling, and retirement.
Support AI model serving, inference optimization, and resource allocation.
Enable model governance, traceability, and operational control across environments.

Develop AI orchestration services to support Agentic AI workflows and enterprise automation.
Implement Retrieval-Augmented Generation (RAG) frameworks and vector database integrations.
Manage Model Context Protocol (MCP) services and AI agent connectivity.
Integrate AI Hub services with AI Products, EDA FY Data Platform, Product Engineering teams, and enterprise applications.
Support API gateways, authentication services, and secure AI service consumption.
Implement LLMOps and MLOps practices for model deployment, monitoring, retraining, and operational management.
Build CI/CD pipelines for AI and machine learning workloads.
Support automated testing, deployment, release management, and infrastructure provisioning.
Establish observability, monitoring, alerting, logging, and performance tracking mechanisms.

Manage cloud and on-premises AI infrastructure environments.
Support GPU infrastructure, resource optimization, workload scheduling, and capacity planning.
Ensure platform availability, reliability, scalability, and disaster recovery readiness.
Troubleshoot platform, infrastructure, model-serving, and integration issues.
Drive continuous improvements in platform stability, security, and performance.

Implement enterprise security controls, access management, authentication, and authorization mechanisms.
Ensure compliance with organizational governance, security, and data privacy policies.
Maintain operational standards and platform best practices.
Support AI governance and responsible AI implementation.

Maintain technical architecture documentation, API specifications, deployment guides, and operational procedures.
Develop coding standards, platform standards, and engineering best practices.
Provide technical support and guidance to internal teams and customer implementations.
Research emerging AI platform technologies, orchestration frameworks, and infrastructure solutions.
Evaluate new tools, platforms, and architectures to improve platform capabilities.
Recommend innovative approaches to enhance AI scalability, performance, and operational efficiency.