Senior AI Platform Engineer

Senior AI Platform Engineer 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. Artech