Senior AI Platform Engineer

Title: Senior AI Platform Engineer

Location: Oaks, PA (Onsite)

Duration: Full-time

Salary Range: $110,000 - $130,000

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 LanEligible to workhain, 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 Eligible to workimization.
• 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): Eligible to workimize 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 adEligible to work AI capabilities.

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