New opportunity

AI / ML Engineer

Accenture · Bengaluru, India

About this role

Accenture seeks an AI / ML Engineer in Bengaluru to lead research and innovation in agentic AI. The role focuses on autonomous, goal-driven systems that reason, plan and interact in complex environments. The project role also includes developing production-ready AI applications using cloud AI services or on-premises pipelines, potentially incorporating generative AI, deep learning, chatbots and image processing.

Responsibilities

Lead R&D on agent architectures, including planning, memory, tool use, self-reflection, human-agent collaboration and model or agent drift detection.
Design and prototype autonomous agents for applications such as enterprise automation, digital assistants and healthcare. Work with solutioning, delivery and engineering teams to integrate these capabilities into scalable systems.
Collaborate with Responsible AI, consulting, academic and industry partners. Publish and present research, support complex client engagements with technical consulting and solution design, and mentor researchers and engineers.

Requirements

Computer Vision is listed as a must-have skill. Deep expertise in reinforcement learning, autonomous agents and multi-agent systems is required, along with an understanding of LLM integration into agentic workflows, experience with frameworks such as LangChain or AutoGPT, and a record of innovation and publications.
A Ph.D. in a listed field, including computer science, electrical engineering, electronics, data science/AI, mathematics or statistics, from a specified institution such as IIT, IISc, IIIT or ISI is required.
The posting gives differing experience figures: the header says 12–14 years, a separate line requires a minimum of 18 years, and the detailed requirements state 15–18 years overall plus 5+ years of relevant post-Ph.D. experience. The position is full-time.

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

Skills for this role

Computer VisionAgentic AIAutonomous agentsMulti-agent systemsReinforcement learningLarge language modelsLangChainAutoGPTAgent planningAgent memoryTool useAgent drift detectionHuman-agent collaborationResponsible AIGenerative AIDeep learningNeural networksCloud AI servicesImage processingAI researchTechnical consultingResearch publicationMentoring

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

Agentic AI Architecture Leadership
The role requires leading R&D for autonomous, goal-driven systems. You must demonstrate deep expertise in planning, memory, tool use, and self-reflection mechanisms to build agents that reason and interact effectively in complex environments.
Production-Grade System Integration
Beyond research, you must bridge the gap between prototypes and scalable, production-ready applications. This involves collaborating with delivery and engineering teams to integrate agentic capabilities into enterprise-level systems while ensuring reliability and performance.
Responsible AI and Governance
You are expected to collaborate with Responsible AI teams to ensure that autonomous systems adhere to ethical standards and safety policies. This requires a proactive approach to managing model and agent drift in real-world deployments.
Thought Leadership and Mentorship
As a senior lead, you must foster a culture of innovation by mentoring researchers and engineers, presenting findings at top AI venues, and providing technical consulting to support complex client engagements.

Likely challenges to think through

Balancing Research Innovation with Enterprise Constraints
The primary challenge is translating cutting-edge, experimental agentic research into stable, scalable enterprise solutions. Prepare by discussing specific strategies for managing technical debt and ensuring system robustness when implementing non-deterministic agent architectures.
Managing Multi-Agent Complexity and Drift
Autonomous multi-agent systems introduce significant complexity regarding coordination and long-term stability. Prepare to explain your methodology for detecting and mitigating agent drift and ensuring consistent performance in dynamic, real-world environments.
Cross-Functional Stakeholder Alignment
You must navigate the intersection of academic research, technical consulting, and delivery operations. Prepare to demonstrate how you communicate complex AI concepts to non-technical clients while maintaining alignment with internal engineering and Responsible AI teams.

Practice your answers

Technical practice

Agentic Architecture Design

What to coverExplain your approach to designing autonomous agents that require multi-step reasoning and planning. Discuss how you handle memory management, tool integration, and self-reflection loops to ensure goal-driven behavior in complex environments.

Multi-Agent System Coordination

What to coverDescribe the challenges of managing communication and task delegation in multi-agent systems. Address how you prevent conflicts and ensure coherence when multiple agents collaborate on a single enterprise automation task.

Agent Drift Detection

What to coverHow do you monitor and detect drift in autonomous agents that utilize LLMs or reinforcement learning? Suggest strategies for identifying when an agent's performance degrades or its decision-making logic deviates from expected parameters.

Computer Vision Integration

What to coverGiven the requirement for Computer Vision, explain how you integrate visual perception into agentic workflows. Discuss the trade-offs between using pre-trained models versus custom architectures for specific image processing tasks within an autonomous system.

Reinforcement Learning in Production

What to coverDiscuss the difficulties of deploying reinforcement learning models in production-ready systems. Focus on stability, reward function design, and how you ensure the agent remains safe and predictable during real-world interactions.

LLM Integration and Frameworks

What to coverCompare the use of frameworks like LangChain or AutoGPT in building scalable agentic systems. Address how you manage prompt engineering, context window limitations, and latency when integrating LLMs into high-frequency decision-making pipelines.

Responsible AI Implementation

What to coverHow do you ensure that autonomous agents adhere to Responsible AI principles? Suggest methods for baking safety, fairness, and transparency into the design phase of agentic architectures.

Scalable System Integration

What to coverDescribe your process for transitioning a research-grade agent prototype into a scalable, production-ready system. Address the infrastructure requirements, such as cloud AI services or on-premises pipelines, needed to support high-concurrency agent operations.

Behavioural practice

Leading Research and Innovation

What to coverDescribe a situation where you led a research initiative that resulted in a novel contribution or publication. Explain how you balanced the need for academic rigor with the practical constraints of a client-facing project.

Mentorship and Talent Development

What to coverProvide an example of how you have mentored junior researchers or engineers to foster a culture of continuous learning. Detail the specific actions you took to help them grow and the impact it had on the team's overall output.

Cross-Functional Collaboration

What to coverTell me about a time you had to collaborate with non-technical stakeholders, such as consulting or delivery teams, to solve a complex client problem. How did you translate technical AI concepts into actionable business solutions?

Navigating Ambiguity in Innovation

What to coverDescribe a project where the requirements were highly ambiguous or the technology was experimental. Explain how you defined the scope, managed expectations, and ensured the project delivered tangible value.

Sharpen your knowledge

Topics to prepare

Agentic AI Architectures
Focus on the theoretical foundations of autonomous systems, specifically planning, memory, and self-reflection mechanisms. Exercise: Draft a high-level architectural design for an enterprise-grade autonomous agent that incorporates a feedback loop for self-correction and drift detection. This is relevant as the role requires leading R&D in these specific architectural components.
Multi-Agent Systems and Collaboration
Study the dynamics of multi-agent coordination, including communication protocols and conflict resolution. Exercise: Analyze a scenario where multiple specialized agents must collaborate to solve a complex healthcare workflow, identifying potential bottlenecks in human-agent interaction. This aligns with the requirement to design systems for complex environments.
Responsible AI in Autonomous Systems
Understand the integration of ethical frameworks into automated decision-making processes. Exercise: Review the principles of Responsible AI and map them to the lifecycle of an autonomous agent, specifically focusing on transparency and accountability in agentic drift detection. This is essential for the required collaboration with the Responsible AI team.

Skills to revise

Reinforcement Learning (RL) Implementation
Apply advanced RL techniques to goal-driven agent behavior. Exercise: Develop a prototype using a standard RL library to train an agent to navigate a simulated environment with sparse rewards. This directly addresses the requirement for deep expertise in reinforcement learning.
LLM Integration with Agent Frameworks
Gain hands-on experience with LangChain or AutoGPT to build agentic workflows. Exercise: Build a functional agent that uses an LLM to select and execute tools based on a user-defined goal, ensuring the agent maintains state across multiple steps. This is a core technical requirement for the role.
Computer Vision for Autonomous Systems
Apply computer vision to enable agents to perceive and interact with complex environments. Exercise: Implement a pipeline that processes visual input to trigger specific agent actions or tool usage. This is explicitly listed as a must-have skill for the position.

Your preparation checklist

Synthesize Agentic AI Research Portfolio
Prepare a summary of your published research and innovation projects, specifically highlighting work in reinforcement learning, multi-agent systems, and autonomous planning. Ensure you can articulate how your past research translates into scalable, production-ready enterprise solutions.
Develop an Agentic Architecture Case Study
Create a technical walkthrough of a complex system you designed that incorporates memory, tool use, and self-reflection. Be prepared to discuss how you addressed agent drift and ensured human-agent collaboration in your design.
Prepare for Responsible AI and Governance Discussions
Review your experience in integrating ethical frameworks into AI development. Prepare to explain your approach to aligning autonomous agent behaviors with Responsible AI policies, particularly in sensitive domains like healthcare or enterprise automation.
Formulate Strategic Questions for Leadership
Draft questions regarding Accenture's current roadmap for agentic AI, such as how the firm balances rapid R&D innovation with the technical constraints of client-facing production pipelines and cross-functional team integration.

Availability last checked: 4 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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