Software Development Engineer.5
Adobe · Bengaluru · Posted 2026-07-08
Tech stack: AWS, Docker, Golang, Kubernetes, Python, Terraform
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About the role
Senior AI Reliability Engineer Develop the AI Framework Driving Adobe's Next Generation of Intelligent Operations At Adobe, we're building an AI-first engineering platform that transforms how software is built, deployed, operated, and secured. 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. What you are going to develop Instead of managing infrastructure, you'll develop the AI platform that manages infrastructure. 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 & 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 utilization, 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 optimization, 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. What we are seeking 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. What makes this role unique You'll participate in building Adobe's AI engineering platform where AI handles infrastructure, explores incidents, detects failures, produces infrastructure, evaluates deployments, performs compliance assessments, and aids every Adobe engineer. This role is not traditional SRE or ordinary MLOps—it's an opportunity to pioneer the future of AI-native Platform Engineering and Agentic Operations on an enterprise scale.
Qualifications
- 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.
- What makes this role unique You'll participate in building Adobe's AI engineering platform where AI handles infrastructure, explores incidents, detects failures, produces infrastructure, evaluates deployments, performs compliance assessments, and aids every Adobe engineer.
- This role is not traditional SRE or ordinary MLOps—it's an opportunity to pioneer the future of AI-native Platform Engineering and Agentic Operations on an enterprise scale.
Responsibilities
- Senior AI Reliability Engineer
- Develop the AI Framework Driving Adobe's Next Generation of Intelligent Operations At Adobe, we're building an AI-first engineering platform that transforms how software is built, deployed, operated, and secured.
- 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.
- What you are going to develop Instead of managing infrastructure, you'll develop the AI platform that manages infrastructure.
- 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 & 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 utilization, 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 optimization, 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.
- What we are seeking 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