Senior AI Platform Engineer

Overview:

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