Senior AI Platform Engineer

Dear Supplier Partners,

Looking for Senior AI Platform Engineer for our client

Location:- Oaks, PA

RoleName - Senior AI Platform Engineer
Skills: AI Agents~AI & Gen AI - Products & Tools
Experience Required: 10 & Above

ROLE_DESCRIPTION -

The Senior AI Platform Engineer will design and engineer the underlying platform capabilities required to develop, deploy, operate, monitor, and scale enterprise AI solutions.

Key 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.

Required 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.
• Experience: 5+ years in software engineering, distributed systems, cloud infrastructure, or the AI/ML lifecycle.
• 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.

Preferred Experience
• 8+ years in platform/cloud/software engineering.
• Production experience supporting enterprise AI/ML or GenAI platforms.
• Experience implementing MLOps or LLMOps at enterprise scale