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POSITION DESCRIPTION We are looking for a AI Specialist with hands-on experience building and deploying production AI applications using Azure OpenAI Services and the OpenAI ecosystem. The ideal candidate has deep expertise in generative AI, large language models (LLMs), multi-agent architectures, and conversational AI with a strong background in Python backend development and cloud-based architecture on Microsoft Azure. You will be responsible for designing, developing, and scaling AI-driven applications that leverage Azure OpenAI and OpenAI APIs to solve complex business problems in a real-world, customer-facing production environment. Models (LLMs), multi-agent architectures, and conversational AI with a strong background in Python backend development and cloud-based architecture on Microsoft Azure. You will be responsible for designing, developing, and scaling AI-driven applications that leverage Azure OpenAI and OpenAI APIs to solve complex business problems in a real-world, customer-facing production environment. ESSENTIAL DUTIES AND TASKS Design and implement AI-powered agents using OpenAI models (GPT-4o, GPT-real time-audio, etc.) and the OpenAI Agents SDK, including multi-agent orchestration with handoff patterns. Develop and maintain a multi-agent system with specialized agents that collaborate through a state-machine-driven booking workflow. Build production-grade prompt engineering systems versioned, modular prompts with step-by-step agent instructions, tool references, and cross-agent handoff logic. Develop secure and scalable REST APIs using FastAPI (Python), including streaming responses (SSE), WebSocket &WebRTC handlers, and middleware for session validation and rate limiting. Implement AI safety and guardrails input/output guardrails for PII detection, jailbreak prevention, discount policy enforcement, and voucher compliance using pattern-matching and LLM-based approaches. Build and maintain the frontend using TypeScript supporting both text chat and voice chat interfaces. Design and manage database layers MongoDB, Redis, and SQL Server. Implement LLM observability using Datadog, Azure Application Insights. Build and maintain CI/CD pipelines using Azure DevOps PR pipelines, CD pipelines, QA and Production deployment pipelines with automated unit testing, code quality checks, and LLM evaluation stages. Collaborate with cloud architects, QA, and product stakeholders to translate business requirements into AI-driven experiences. Provide technical mentoring and code review guidance to team members, maintaining comprehensive documentation and coding standards. PROFESSIONAL SKILLSET QUALIFICATIONS Bachelors or Masters degree in Computer Science, Data Science, AI/ML, or a related field. 3+ years of experience in AI/ML engineering, with 2+ years focused on Gen-AI (Azure OpenAI, OpenAI APIs) or similar LLM platforms. Strong programming skills in Python with type annotations, and experience with FastAPI, Pydantic, async/await patterns, and modern Python tooling (UV, Ruff, MyPy). Expertise in Microsoft Azure Azure OpenAI, Azure Identity, Azure Redis Cache, Azure DevOps Pipelines. Hands-on experience with the OpenAI Agents SDK or similar agent frameworks multi-agent orchestration, tool calling, function tools, agent handoffs, and guardrails. Production experience building conversational AI / chatbot applications with real-world usage in NLP, booking flows, or customer-facing domains. Experience with MongoDB, Redis, and SQL Server for database operations in production applications. Experience building and deploying containerized applications using Docker and Docker Compose. Deep understanding of AI safety and responsible AI input/output guardrails, PII detection/redaction, jail break prevention, and policy compliance enforcement. Strong experience with prompt engineering versioned prompts, multi-step instructions, few-shot examples, tool description optimization, and cross-agent prompt coordination. Experience with LLM observability and monitoring Datadog, tracing, span creation, token usage tracking, and structured logging. Strong analytical, problem-solving, and debugging skills with a focus on code quality, test coverage, and production reliability. PREFERRED QUALIFICATIONS Microsoft certifications (e.g., Azure AI Engineer Associate, Azure Solutions Architect Expert). Experience with MLOps model evaluation pipelines, LLM evaluation frameworks, experiment tracking. Experience with OpenAI Realtime API WebRTC-based voice interactions, real-time speech-to-text and text-to-speech. Knowledge of Azure Kubernetes Service (AKS), Azure Functions, or serverless deployment patterns. Experience with pre-commit hooks, code quality automation (Ruff, MyPy, ESLint, Prettier), and Git-based workflowenforcement. Familiarity with Agile/Scrum methodologies and Azure DevOps Boards for project .
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.