Senior AI Platform Engineer

Job Description Must Have Technical/Functional Skills Strong cloud engineering and AI/ML platform engineering experience. Kubernetes, containers, Infrastructure as Code, CI/CD, and API technologies. Python and software engineering experience. Experience with ML/LLM deployment and GenAI frameworks. Knowledge of vector databases, embedding models, model APIs, and AI orchestration. Experience with monitoring, telemetry, security, and production support. Programming: Advanced proficiency in Python; additional experience in Go, C++, or Rust is often preferred. Infrastructure & Cloud: Hands-on expertise with Kubernetes, Docker, and cloud platforms like AWS (SageMaker, Bedrock) or Azure (AI Foundry, OpenAI). AI Frameworks: Familiarity with orchestration and LLM tooling such as LangChain, LangGraph, Ray, or Kubeflow. Infrastructure-as-Code (IaC): Knowledge of automation tools like Terraform and Ansible. Roles & Responsibilities Build and maintain enterprise AI/ML and GenAI platform capabilities. Implement model endpoints, gateways, orchestration layers, and AI services. Build infrastructure supporting LLMs, SLMs, RAG, embeddings, vector stores, and AI agents. Develop automated deployment and CI/CD pipelines for AI applications and models. Implement LLMOps/MLOps capabilities covering deployment, monitoring, evaluation, versioning, and observability. Implement model-routing and inference-management capabilities. Support token, compute, latency, and inference-cost optimization. Integrate AI platforms with enterprise identity, security, logging, monitoring, networking, and API-management services. Automate infrastructure provisioning and platform configuration. Establish production reliability, scalability, resilience, and operational standards. Partner with AI architects, application engineers, data engineers, and security teams. Platform Architecture: Design and deploy scalable, cloud-native or bare-metal infrastructure (using Kubernetes, OpenShift, or AWS/Azure) to support large language models (LLMs) and machine learning workloads. Model Operations (MLOps/LLMOps): Optimize model serving, inference performance, GPU utilization, and automated pipelines for fine-tuning and deploying AI models. Agentic & GenAI Integration: Build and maintain shared services like AI gateways, Retrieval-Augmented Generation (RAG) frameworks, and autonomous agent orchestration platforms. Governance & Security: Enforce enterprise data protection, compliance, auditability, and cybersecurity standards across all AI tooling. Developer Enablement: Create self-service platforms, APIs, and monitoring/observability tools so internal software and data science teams can safely adopt AI capabilities. TCS Employee Benefits Summary Discretionary Annual Incentive. Comprehensive Medical Coverage: Medical & Health, Dental & Vision, Disability Planning & Insurance, Pet Insurance Plans. Family Support: Maternal & Parental Leaves. Insurance Options: Auto & Home Insurance, Identity Theft Protection. Convenience & Professional Growth: Commuter Benefits & Certification & Training Reimburseme nt. Time Off: Vacation, Time Off, Sick Leave & Holidays. Legal & Financial Assistance: Legal Assistance, 401K Plan, Performance Bonus, College Fund, Student Loan Refinancing. Salary Range $110,000 - $130,000 a year Qualifications: BACHELOR OF COMPUTER SCIENCE Show more Show less