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You will design, build, and maintain Agentic AI systems that integrate LLMs with tools, APIs, databases, and enterprise workflows to enable intelligent automation and action-driven AI execution. You will build and optimize AI agents capable of multi-step reasoning, tool usage, and workflow execution across business processes. You will develop and maintain orchestration frameworks such as LangGraph, LangChain, PydanticAI, MCP, OpenClaw, OpenCode, or similar technologies for scalable agent operations. You will build and optimize RAG pipelines, embeddings, prompt structures, and contextual retrieval logic to improve response quality and execution reliability. You will work with LLM models, MoE models, embedding models, and visual models based on enterprise use case needs. You will engineer backend services, APIs, and integrations using Python, Node.js, and JavaScript so AI agents can interact with internal systems, databases, and workflow tools. You will automate Agentic AI workflows using platforms such as n8n or similar tools, with clear retry, fallback, and exception-handling logic. You will deploy and operationalize AI agents using Docker/Podman, CI/CD pipelines, and production-ready serving environments. You will monitor model and workflow performance using observability, logging, and auditability controls to ensure safe and reliable operations. You will collaborate closely with Data, Platform, DevOps, and Business teams to translate requirements into production-ready Agentic AI solutions. You will maintain high-quality technical documentation for architecture, prompts, tool integration, deployment, and operational procedures. Excellent communication, collaboration, and documentation skills, with fluency in English. Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, or a related field. 3+ years of hands-on experience in AI/ML engineering, AI application development, or workflow automation. Strong proficiency in Python, Node.js, and JavaScript, with proven ability to build backend services, APIs, and production-grade integrations. Hands-on experience with LLMs, RAG pipelines, prompt engineering, and agent orchestration frameworks. Experience building Agentic AI workflows that connect models with tools, APIs, databases, or business systems. Practical experience with LangGraph, LangChain, PydanticAI, MCP, vLLM, OpenClaw, OpenCode, or similar technologies. Familiarity with LLM models, MoE models, embedding models, and visual models for enterprise AI implementation. Experience using orchestration tools such as n8n or equivalent automation platforms. Ability to deploy AI systems in production using containerization and CI/CD pipelines. Understanding of inference monitoring, model lifecycle, evaluation, observability, and governance. Exposure to Big Data platforms such as Apache NiFi, Hortonworks/Cloudera, or Apache Ranger. Familiarity with data lakehouse or distributed data ecosystems supporting analytics and AI workloads. Experience with vector databases and enterprise retrieval systems. Familiarity with OpenWebUI, LibreChat, or other enterprise conversational AI interfaces. Experience with on-prem/private AI architecture, observability tools, and API gateways.
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.