Mu Sigma - AI Engineer- GenAI & Multi-Agent Systems

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 isn't keeping pace with the rate of change in business. This is where welcome in - we address the gaps. Mu Sigma is the world's 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

Location- Bangalore (White field)

Work mode- 5 WFO

Timmings- 12pm- 9pm

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 execution

Develop applications using LLMs (GPT, Claude, Llama, etc.) with advanced prompt engineering and orchestration

Implement Agentic workflows (planner executor critic memory loops)

Build RAG (Retrieval-Augmented Generation) pipelines with vector databases for enterprise knowledge grounding

Develop tool-using agents that integrate with APIs, databases, and enterprise systems

Architect and deploy AI copilots and autonomous assistants for business workflows

Optimize LLM performance using fine-tuning, prompt chaining, and caching strategies

Implement 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

Bachelor's/master's in computer science, AI, or related field

3-8 years experience in AI/ML with strong focus on Generative AI

Strong Python development skills

Hands-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 agents

Strong 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 pipelines

Systems thinking for designing autonomous AI architectures

Strong problem decomposition for agent task design

Ability to balance latency, cost, and accuracy in LLM systems

Communication with business stakeholders to translate workflows into agent pipelines

Innovation mindset with focus on applying agentic AI in production

Preferred Qualifications

Experience building multi-agent orchestration systems with role-based coordination

Exposure 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 graphs

Building autonomous systems or copilots in enterprise environments

Domain experience in industrial, energy, or IoT environments

Tech Stack (Modern GenAI Stack)

Languages: Python

Frameworks: LangChain, CrewAI, AutoGen, Semantic Kernel

LLMs: OpenAI GPT, Azure OpenAI, Claude, Llama

Vector DB: Pinecone, Weaviate, FAISS

Orchestration: Airflow, Prefect

Deployment: Docker, Kubernetes

Cloud: Azure AI Studio / Azure ML (preferred)

KPIs / Success Metrics

Autonomous task completion rate of agents

Reduction in manual workflows via AI automation

Latency and cost optimization of LLM pipelines

Accuracy and reliability of agent outputs

Adoption rate of AI copilots across teams