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Mu Sigma Business Solutions Pvt. Ltd. Mu Sigma Business Solutions Pvt. Ltd Big Data tops many a list of business priorities, thanks to its growing volume, velocity, and variety. However, it is not data, but change that is the cause of anxiety to organization, bringing with it greater complexity and more data. The challenge here is that analytical thinking isnt keeping pace with the rate of change in business. This is where welcome in we address the gaps. Mu Sigma is the worlds largest pure play Decision Sciences and analytics firm. We help over 140 Fortune 500 clients across more than 10 industry verticals, to institutionalize data-driven decision making in a cost-effective and scalable manner. We provide our clients with a holistic ecosystem of proprietary technology platforms, processes and people.Our unique approach to problem solving using cross-industry expertise corroborates our sustainable engagement model with our clients still further, making us one of the most preferred analytics and Decision Sciences partners. With over 3500 Decision Sciences professionals, we pride ourselves in being a category and career defining company. As we continue to scale, we are also looking at hiring the right talent across various levels in our organization. Role Overview We are looking for a highly skilled AI Engineer specializing in Generative AI and Multi-Agent Systems to design and deploy intelligent, autonomous solutions. This role focuses on building LLM-powered, agent-driven architectures that can reason, collaborate, and execute complex workflows across enterprise systems.You will work on cutting-edge Agentic AI frameworks, enabling systems that go beyond prediction to decision-making, orchestration, and autonomous execution. Key Responsibilities Design and build multi-agent AI systems capable of planning, reasoning, and task executionDevelop applications using LLMs (GPT, Claude, Llama, etc.) with advanced prompt engineering and orchestrationImplement Agentic workflows (planner executor critic memory loops)Build RAG (Retrieval-Augmented Generation) pipelines with vector databases for enterprise knowledge groundingDevelop tool-using agents that integrate with APIs, databases, and enterprise systemsArchitect and deploy AI copilots and autonomous assistants for business workflowsOptimize LLM performance using fine-tuning, prompt chaining, and caching strategiesImplement short-term and long-term memory mechanisms (vector stores, knowledgegraphs)Design multi-agent collaboration protocols (hierarchical, swarm, role-based agents)Deploy scalable solutions using MLOps + LLMOps practices (monitoring, evaluation,guardrails)Ensure AI safety, governance, and responsible AI practices Required Skills & Competencies Bachelors/masters in computer science, AI, or related field38 years experience in AI/ML with strong focus on Generative AIStrong Python development skillsHands-on experience with: o LLMs & GenAI frameworks: OpenAI, Hugging Face Transformers o Agent frameworks: LangChain, AutoGen, CrewAI, Semantic Kernel o RAG pipelines & vector DBs: FAISS, Pinecone, Weaviate Experience building API-driven, tool-integrated AI agentsStrong understanding of: o Prompt engineering & prompt optimization o Chain-of-thought reasoning and tool augmentation o Context management and token optimization Experience with cloud platforms (Azure OpenAI preferred, AWS/GCP acceptable)Knowledge of Docker, Kubernetes, CI/CD pipelinesSystems thinking for designing autonomous AI architecturesStrong problem decomposition for agent task designAbility to balance latency, cost, and accuracy in LLM systemsCommunication with business stakeholders to translate workflows into agent pipelinesInnovation mindset with focus on applying agentic AI in production Preferred Qualifications Experience building multi-agent orchestration systems with role-based coordinationExposure to agent planning algorithms (ReAct, Plan-and-Execute, Tree-of-Thought)Experience with LLM evaluation frameworks (RAGAS, TruLens, Promptfoo)Knowledge of graph-based reasoning / knowledge graphsBuilding autonomous systems or copilots in enterprise environmentsDomain experience in industrial, energy, or IoT environments Tech Stack (Modern GenAI Stack) Languages: PythonFrameworks: LangChain, CrewAI, AutoGen, Semantic KernelLLMs: OpenAI GPT, Azure OpenAI, Claude, LlamaVector DB: Pinecone, Weaviate, FAISSOrchestration: Airflow, PrefectDeployment: Docker, KubernetesCloud: Azure AI Studio .
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.