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Responsibilities: End-to-end design, development, and deployment of enterprise-grade AI solutions leveraging Azure AI, Google Vertex AI, or comparable cloud platforms. Architect and implement advanced AI systems, including agentic workflows, LLM integrations, MCP-based solutions, RAG pipelines, and scalable microservices. Oversee the development of Python-based applications, RESTful APIs, data processing pipelines, and complex system integrations. Define and uphold engineering best practices, including CI/CD automation, testing frameworks, model evaluation procedures, observability, and operational monitoring. Partner closely with product owners and business stakeholders to translate requirements into actionable technical designs, delivery plans, and execution roadmaps. Provide hands-on technical leadership, conducting code reviews, offering architectural guidance, and ensuring adherence to security, governance, and compliance standards. Communicate technical decisions, delivery risks, and mitigation strategies effectively to senior leadership and cross-functional teams. Required Skills & Experience: LLM & Core AI Strong understanding of transformers (attention, tokens, context window) and LLM behavior. Hands-on with 2+ LLM providers (e.g., Azure OpenAI + Anthropic / open source like Llama/Qwen). Experience tuning decoding parameters and handling context window limits (truncation, sliding window, summarization). Prompting & Context Engineering Proven experience designing multi-layer prompts (system/policy, task, user, tools, retrieved context). Built context builders that select relevant history (recency + semantic) and inject tool + RAG outputs. Implemented context compression (conversation/memory summarization) and structured outputs (JSON/schema) with robust error handling. Tools, MCP & External Integrations Designed and implemented LLM tools/function schemas with validation, clear errors, and safe side-effects. Hands-on experience with MCP (Model Context Protocol): building MCP servers/tools for internal data and actions, including auth and multi-tenant isolation. Experience integrating REST/SQL/sandboxed execution tools and defining fallback/degradation strategies when tools fail. Agentic Systems, Orchestration & A2A Built multi-step agentic workflows: plan tool calls intermediate decisions final answer. Practical use of agent roles (Planner / Worker / Critic / Router / Supervisor). Hands-on with A2A (Agent-to-Agent) collaboration where specialist agents exchange structured state. Experience with at least one agentic/workflow framework (e.g., LangGraph, LangChain agents, Google ADK, Orkes Conductor, Temporal) and checkpointed, resumable flows (Postgres/Redis). RAG & Knowledge Orchestration Delivered end-to-end RAG systems: ingestion chunking embedding indexing retrieval synthesis. Implemented hybrid search (vector + keyword + filters) over enterprise sources (PDF, HTML, Confluence/SharePoint, SQL). Experience with query rewriting/expansion and grounded answers with citations, including debugging retrieval quality. Reasoning, Evaluation & Guardrails Implemented ReAct-style and tool-augmented reasoning patterns, including self-critique/second-pass flows. Defined task-level success metrics and built golden test flows from real logs to evaluate prompt/model/flow changes. Instrumented telemetry for tool errors, step counts, loops, latency, and cost (tokens, per feature/tenant). Implemented guardrails: prompt-injection defenses, per-tenant/per-role tool & data access, input/output filtering, PII-safe logging, and participated in red teaming/adversarial testing. Model, Cost & Performance Engineering Experience choosing and combining small router/classifier models with large reasoning models. Implemented caching (LLM outputs, retrieval results) and optimized latency (parallelization, step count, time budgets). Built or contributed to cost/usage monitoring for LLM and agent workflows. Supporting Software Engineering Expert-level proficiency in Python, RESTful API development, microservices architecture, and containerized deployments (Kubernetes, Docker). Experience with API frameworks such as FastAPI, FastMCP, Fl .
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.