New opportunity

S&C Global Network - AI - CDI - Data Science Consultant

Accenture · Gurgaon, India

About this role

As an AI Decision Science Consultant at Accenture, you will lead the design, development, and deployment of sophisticated AI agents and multi-modal AI solutions capable of autonomously executing complex business workflows. You will leverage cutting-edge Generative AI, Agentic AI frameworks, and Model Context Protocol (MCP) integrations to create intelligent systems that reason, plan, and act independently. Responsibilities include: Advanced Generative AI & Agentic System Development: Design and implement sophisticated AI agents using frameworks like AI Refinery, LangChain, AutoGen, and CrewAI. Develop multi-agent systems for autonomous collaboration and execution. Implement MCP integrations for communication between AI systems and external tools. Build and deploy Generative AI applications with advanced reasoning capabilities. Data Science & Model Implementation: Develop solutions using Computer Vision, Machine Learning, and LLMs. Define data requirements, perform data analysis, and conduct quality assessments. Train and fine-tune LLMs using large-scale datasets while evaluating performance for iterative improvements. Solution Integration: Collaborate to integrate AI solutions into end-to-end workflows, ensuring scalability using cloud platforms like AWS, Azure, or GCP. Innovation: Stay updated on AI advancements, document methodologies, and share knowledge across the team. Requirements: 5-10 years of experience. Expert proficiency in Generative AI, Agentic AI Systems, MCP, LLMs, Multi-Agent Systems, Python, SQL, and AI Agent Orchestration. Strong understanding of AI safety, alignment, and governance principles for autonomous systems. Experience with enterprise-grade AI agent deployments and production system management. Proficiency in cloud infrastructure (AWS/Azure/GCP), Docker, Kubernetes, and Spark. Knowledge of NLP, Computer Vision, and Reinforcement Learning is highly valued. This position is based at the Gurugram office.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Generative AIAgentic AI SystemsModel Context Protocol (MCP)Large Language Models (LLMs)Multi-Agent SystemsPythonSQLAI Agent OrchestrationAI RefineryLangChainLlamaIndexAutoGenCrew AISparkAWSAzureGCPNLPComputer VisionVector DatabasesRAGFunction CallingTool IntegrationDockerKubernetesMLOpsReinforcement LearningMultimodal AI

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Architecting Autonomous Agentic Workflows
The role requires designing systems that move beyond simple chat interfaces to autonomous execution of business workflows. You must demonstrate proficiency in orchestrating multi-agent systems using frameworks like AutoGen or CrewAI, ensuring agents can reason, plan, and collaborate to complete complex tasks independently.
Enterprise-Grade AI Governance and Safety
Because these agents operate autonomously, you must prioritize AI safety, alignment, and governance. You will need to explain how you implement guardrails, manage model drift, and ensure that autonomous decision-making remains within the bounds of enterprise compliance and risk management frameworks.
Integration via Model Context Protocol (MCP)
A core technical priority is the implementation of MCP to bridge AI systems with external tools and APIs. You should be prepared to discuss how you standardize communication between LLMs and enterprise data sources to ensure seamless, scalable integration into existing production environments.

Likely challenges to think through

Balancing Autonomy with Reliability
The primary challenge is the inherent unpredictability of autonomous agents in production. Prepare by discussing your approach to MLOps for agentic systems, specifically how you monitor performance, handle failure states, and implement iterative fine-tuning to maintain high accuracy in complex business processes.
Managing Multi-Modal System Complexity
Integrating Computer Vision, NLP, and LLMs into a single cohesive agentic architecture introduces significant technical overhead. You should prepare to explain how you manage data pipelines, ensure model interoperability, and optimize cloud infrastructure (AWS/Azure/GCP) to support these resource-intensive, multi-modal workflows.

Practice your answers

Technical practice

Designing Multi-Agent Orchestration

What to coverExplain how you would architect a multi-agent system using frameworks like CrewAI or AutoGen. Focus on how you manage agent communication, task delegation, and conflict resolution when agents have overlapping responsibilities.

Implementing Model Context Protocol (MCP)

What to coverDescribe the role of the Model Context Protocol in connecting LLMs to external tools. Discuss how you ensure secure, standardized data exchange between an AI agent and enterprise APIs or databases.

LLM Fine-Tuning and Evaluation

What to coverDetail your process for fine-tuning an LLM for a specific business domain. Address how you define success metrics, conduct quality assessments, and manage the trade-off between model performance and computational cost.

AI Safety and Governance

What to coverDiscuss the principles of AI alignment and safety when deploying autonomous agents in production. Explain how you implement guardrails to prevent hallucination, bias, or unauthorized actions in business workflows.

Scalable Deployment on Cloud Infrastructure

What to coverExplain how you deploy and scale agentic AI applications using Kubernetes and cloud platforms like AWS or Azure. Focus on managing state, latency, and resource allocation for long-running autonomous processes.

Reinforcement Learning for Agent Training

What to coverDescribe how you apply Reinforcement Learning (RL) to improve agent decision-making. Explain the challenges of defining reward functions in complex, multi-step business workflows.

Integrating Computer Vision with LLMs

What to coverDiscuss the architecture required for a multi-modal AI system that processes visual data to inform agentic actions. Address how you handle data ingestion and feature extraction before passing context to the LLM.

MLOps for Agentic Systems

What to coverExplain how MLOps pipelines differ for agentic systems compared to traditional predictive models. Focus on monitoring agent performance, versioning agent prompts, and handling iterative updates in production.

Behavioural practice

Managing Complex Stakeholder Requirements

What to coverDescribe a situation where you had to translate ambiguous business requirements into a technical roadmap for an AI solution. Focus on how you balanced client expectations with the technical limitations of current AI frameworks.

Leading Technical Innovation

What to coverProvide an example of when you introduced a new AI framework or methodology to your team. Explain how you documented the process and ensured knowledge transfer to maintain project continuity.

Handling Production Failures in Autonomous Systems

What to coverDescribe a time when an autonomous system you deployed behaved unexpectedly in production. Detail the steps you took to diagnose the root cause, mitigate the impact, and improve the system's governance.

Collaborating Across Functional Teams

What to coverShare an experience where you collaborated with non-technical stakeholders to integrate an AI solution into an existing business workflow. Explain how you ensured the solution was scalable and aligned with organizational goals.

Sharpen your knowledge

Topics to prepare

Agentic AI Orchestration and Reasoning
The role requires designing systems that reason, plan, and act autonomously. Review the conceptual differences between simple LLM chains and multi-agent systems where agents collaborate and negotiate. Practice mapping a complex business workflow into a multi-agent architecture to demonstrate your understanding of agentic autonomy.
AI Safety, Alignment, and Governance
Accenture emphasizes the need for responsible AI in autonomous systems. Study frameworks for AI alignment and governance to ensure agentic systems remain within defined constraints. Prepare to discuss how you would implement guardrails and monitoring for enterprise-grade deployments to prevent unintended autonomous behaviors.
Model Context Protocol (MCP) Integration
MCP is a specific requirement for this role to facilitate communication between AI systems and external tools. Research the protocol's architecture and how it standardizes data exchange. Practice designing a scenario where an agent uses MCP to securely interact with an external API or database.

Skills to revise

Multi-Agent Framework Implementation
The role requires proficiency in frameworks like LangChain, AutoGen, and CrewAI. Build a prototype using one of these frameworks that involves at least two agents with distinct roles (e.g., a researcher and a writer) collaborating to complete a task. This directly addresses the requirement for multi-agent system development.
Cloud-Native AI Deployment
You must be able to deploy AI solutions on AWS, Azure, or GCP using Docker and Kubernetes. Create a containerized version of a simple LLM application and define a basic Kubernetes deployment manifest. This demonstrates your ability to handle the production system management and scalability requirements mentioned in the job description.
LLM Fine-Tuning and Evaluation
The position involves training and fine-tuning models for specific performance improvements. Practice the end-to-end process of preparing a dataset, fine-tuning a small open-source LLM, and establishing an evaluation pipeline to measure performance gains. This is essential for the data science and model implementation responsibilities.

Your preparation checklist

Prepare a Multi-Agent System Case Study
Draft a technical walkthrough of a project where you designed an autonomous multi-agent system. Focus on how you used frameworks like LangChain, AutoGen, or CrewAI to enable agents to collaborate, reason, and execute a specific business workflow.
Demonstrate Model Context Protocol (MCP) Expertise
Prepare a clear explanation of how you have implemented or would implement MCP to connect AI systems with external tools and APIs. Be ready to discuss the architectural benefits of MCP for interoperability in enterprise environments.
Articulate AI Governance and Safety Frameworks
Develop a structured approach for ensuring AI safety, alignment, and governance in autonomous systems. Prepare to discuss how you mitigate risks such as hallucination, bias, and unauthorized execution in production-grade deployments.
Map Cloud and MLOps Infrastructure Experience
Create a summary of your experience deploying AI solutions on AWS, Azure, or GCP. Specifically, highlight your proficiency in using Docker, Kubernetes, and MLOps pipelines to ensure the scalability and reliability of agentic AI applications.
Formulate Strategic Questions for the Interviewer
Prepare questions regarding the team's current approach to agent orchestration, the specific business domains they are targeting for autonomous workflows, and how they balance rapid AI innovation with Accenture's enterprise governance standards.

Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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