Senior AI Reliability Engineer

We're seeking engineers enthusiastic about LLMs, AI Agents, MLOps, Kubernetes, cloud infrastructure, and autonomous operations to develop the AI platform driving the future of Adobe's engineering ecosystem. You will operate where AI, Platform Engineering, SRE, and Cloud Infrastructure meet. You will create intelligent systems that automate operations, speed up developer productivity, and support AI-powered decision-making on a large scale.
The candidate will have responsibilities across the following functions:
AI Platform Engineering:
Design and build enterprise AI platforms for deploying, operating, and scaling LLM-powered applications.
Develop AI Agents and MCP (Model Context Protocol) servers that automate engineering workflows.
Build secure AI gateways enabling developers to interact with infrastructure through natural language.
Design Retrieval-Augmented Generation (RAG) pipelines using enterprise knowledge bases.
Build AI copilots for incident response, debugging, deployment validation, and operational workflows.
Develop AI assessment frameworks to consistently gauge model quality, accuracy, latency, cost, and safety.
MLOps or equivalent experience and LLMOps:
Build production-grade inference platforms for foundation models and AI services.
Deploy and manage LLM workloads on Kubernetes.
Design scalable GPU-enabled infrastructure for model inference and AI workloads.
Build CI/CD pipelines for AI models including automated testing, evaluation, rollout, and rollback.
Automate model lifecycle management including deployment, versioning, monitoring, and governance.
Implement prompt versioning, model experimentation, and AI release management.
AI Observability:
Build observability platforms for AI systems including model latency, token utilisation, cost per request, hallucination detection, prompt quality, AI workflow tracing, and agent execution monitoring.
Build intelligent anomaly detection using AI.
Autonomous Operations:
Build systems capable of autonomous incident investigation, root cause analysis, infrastructure provisioning, automated remediation, capacity optimisation, AI-assisted change management, and predictive failure analysis.
Platform Engineering:
Continue building Adobe's cloud platform by bringing to bear Kubernetes, AWS, Terraform, GitOps, ArgoCD, Service Mesh, Platform APIs, Internal Developer Platforms, and multi-region infrastructure.
Requirements:
AI Engineering: Practical experience working directly with one or more of the following: OpenAI APIs, Anthropic Claude, LangGraph, LangChain, MCP, RAG, Vector Databases, Agentic AI, Prompt Engineering, and AI Evaluation Frameworks. Background in developing AI-native applications instead of only using AI APIs.
MLOps or equivalent experience: Experience with: model serving, ML pipelines, feature stores, model monitoring, MLflow, Kubeflow, Ray, KServe, BentoML, Triton Inference Server, GPU scheduling, and the NVIDIA ecosystem.
Platform Engineering: Strong experience with: Kubernetes, AWS, Terraform, Docker, Helm, ArgoCD, GitOps or equivalent experience, and Platform Engineering.
Programming: Excellent programming skills in Python and Golang. Experience building distributed systems and APIs.
Observability: Experience with: Prometheus, Grafana, OpenTelemetry, Jaeger, New Relic, and Elastic.
Bonus:
Experience building: AI Agents, MCP Servers, AI Gateways, Developer Copilots, AI-assisted IDE tooling, autonomous remediation systems, and AI-powered deployment platforms.

Experience
6-9 yrs