Description

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

Key Skills

Education

Bachelor's degree