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Overview The AI Observability Engineer (Agentic Frameworks & AI Agent Operations Center Developer) builds and operationalizes agentic AI solutions using modern orchestration frameworks and contributes to an AI Agent Operations Center that enables safe, reliable, and observable agent behavior at scale. This role focuses on developing agent workflows (planning, tool execution, memory, and RAG), integrating guardrails and evaluations, and delivering operational capabilities such as run management, telemetry, and incident triage for production agents. Responsibilities AI Agent Operations Center (70%) Build operations center capabilities for agent runtime management: agent registry, versioning, deployment tracking, and run histories Enable operational workflows such as incident triage, replay/debug runs, trace correlation, and root-cause analysis across agent steps Implement operational dashboards and views for agent health: success rate, latency, tool failure rate, cost per run, and loop detection Instrument agent flows end-to-end using OpenTelemetry (or equivalent), enabling correlation across prompts, tool calls, retrieval, and responses Implement semantic conventions and tagging standards (agent name/version, tool name, model provider, environment, tenant/app) Partner with SRE/observability teams to ensure production-grade monitoring, alerting, and operational readiness Collaboration with Teams (10%) Collaborate with transformation teams and business stakeholders to understand requirements and tailor AI agents to specific domains. Work closely with AI platform teams to build scalable and cross-domain AI agents while ensuring end-to-end observability. Integration & Deployment (10%) Build and maintain CI/CD pipelines for agent services and operations center components, including automated testing and deployment Automate onboarding for new agent use cases (templates, scaffolding, configuration checks) Drive best practices for secure, scalable, and cost-effective agent deployments Continuous Learning (10%) Stay updated with the latest advancements in AI and machine learning technologies and integrate these into existing or new AI agents. Conduct thorough testing and validation to ensure the reliability and accuracy of AI agents and solutions. Qualifications Key Skills/Experience Required Minimum Qualifications: Education: Bachelors in Computer Science, AI/ML, Data Science, or a related field. Experience: 35+ years of software engineering experience; 1+ years building and observe AI/ML or GenAI applications preferred Required Expertise Hands-on experience with agentic frameworks (Crew.ai, LangChain, Semantic Kernel, AutoGen, or similar) Proficiency in Python (primary) and familiarity with APIs/microservices patterns Strong experience with RAG patterns (embeddings, vector search, retrieval evaluation, chunking strategies) Experience with cloud environments (Azure/AWS/GCP) and containerized deployments (Kubernetes/AKS/EKS) Familiarity with observability fundamentals (logs/metrics/traces) and production troubleshooting Experience building internal developer platforms or operational consoles (agent registry, run tracking, dashboards) Familiarity with OpenTelemetry, distributed tracg, and telemetry pipelines Experience with Azure AI Search / vector databases, prompt/version management, and evaluation frameworks Knowledge of Responsible AI practices: data handling, safety guardrails, audit trails, and redaction strategies FinOps exposure: token/GPU cost optimization and chargeback/showback reporting Technical Proficiency: Agent orchestration design (planning, tool execution, memory, RAG), Strong engineering discipline: testing, versioning, CI/CD, automation, Operational mindset: reliability, debuggability, and incident response support Problem-Solving: Ability to translate business challenges into technical solutions. Collaboration Skills: Effective at working within cross-functional teams. Agility: Flexibility to adapt to changing requirements and new technologies. Communication Skills: Capable of explaining complex technical concepts to non-technical stakeholders. .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.