Job Description

Position Summary:

We are seeking a visionary

Agentic AI Architect

to lead the design, development, and deployment of autonomous AI systems that perceive, reason, and act across complex enterprise workflows. This role is ideal for someone passionate about building intelligent agents that operate with minimal human intervention, while ensuring safety, scalability, and alignment with business goals.


You will architect LLM-powered agents, define robust testing strategies, and drive innovation across agentic automation platforms such as Microsoft Copilot Studio, AWS Agents, and LangGraph

Job Functions and Responsibilities:

AI Architecture & Agent Design

Architect intelligent agents using LLMs/AWS Bedrock, orchestration frameworks (LangChain, LangGraph), and tool integrations (APIs, databases). Enable autonomous task planning, decision-making, and long-running process management. Implement memory, context management, prompt engineering, and feedback loops. Establish observability, logging, and feedback loops for continuous agent improvement.

Testing & Validation

Develop comprehensive testing frameworks for agentic systems including unit, integration, regression, and safety tests. Design simulation environments to evaluate ethical guardrails, hallucination risks, and performance boundaries. Collaborate with QA, risk, compliance, and product teams to align agent behavior with legal and user expectations.

Platform Strategy & Innovation

Evaluate and prototype agentic frameworks (e.g., CrewAI, AutoGen, Google ADK, etc). Lead integration of R&D into production architectures. Define architectural blueprints and contribute to internal and external thought leadership. Stay current with advancements in cognitive architectures, multi-agent systems, and AI safety
In addition to technical expertise, an Agentic AI Architect must possess a strong set of soft skills to effectively lead the design and deployment of intelligent agent systems. These include:


Systems Thinking - to design holistic, interconnected agent ecosystems. Strategic Communication - to articulate complex AI concepts to both technical and non-technical stakeholders. Collaboration & Cross-Functional Leadership - to align AI initiatives across product, engineering, and compliance teams. Creative Problem Solving - to innovate around the limitations of current LLMs and agentic frameworks. Adaptability & Learning Agility - to stay ahead in a rapidly evolving AI landscape. Empathy & User-Centric Thinking - to ensure agents are designed with human needs and usability in mind. Decision-Making Under Uncertainty - to guide agent behavior in dynamic or ambiguous environments. Documentation & Knowledge Sharing - to promote reproducibility and team-wide understanding of agentic systems. Ethical Reasoning - to ensure responsible AI development and deployment. Influence Without Authority - to drive adoption and alignment without direct control over all stakeholders.

Key Result Areas:

1. Agentic System Design & Architecture

+ Design scalable, modular, and secure multi-agent architectures using agentic frameworks like AWS Bedrock, LangGraph etc.
+ Define agent roles, workflows, and interaction protocols aligned with business objectives.

2. LLM Integration & Orchestration

+ Integrate large language models (LLMs) into agentic systems with memory, tools, and reasoning capabilities.
+ Optimize prompt engineering, context management, and tool usage for performance and cost-efficiency.

3. Cross-Functional Collaboration

+ Partner with product, engineering, data science, and compliance teams to ensure agentic systems meet functional and regulatory requirements.
+ Translate business needs into agentic workflows and technical specifications.

4. Innovation & Experimentation

+ Lead rapid prototyping and evaluation of new agentic frameworks, tools, and cognitive models.
+ Benchmark agent performance and iterate on design for continuous improvement.

5. Governance, Safety & Ethics

+ Implement safety guardrails, observability, and human-in-the-loop mechanisms.
+ Ensure compliance with ethical AI principles, data privacy, and organizational policies.

6. Documentation & Knowledge Sharing

+ Maintain clear documentation of agent designs, SOPs, and architectural decisions.
+ Conduct internal workshops or demos to scale agentic AI literacy across teams.

7. Business Impact & Value Delivery

+ Measure and report on the ROI of agentic systems in terms of automation, efficiency, and innovation.
+ Identify new opportunities for agentic AI to drive digital transformation.

Qualifications:

Bachelor's or Master's in AI/ML, or Data Science. At least 5+ years of relevant experience in AI/ML solutions development. At least 2+ years of relevant experience in AI Agentic automation solutions development. Proficiency in Python, LangChain, LangGraph, AWS Bedrock, Hugging Face, similar LLM frameworks Experience with agent architecture, vector stores, tool calling, and memory management. Familiarity with MLOps, model evaluation metrics, and safety techniques for generative AI. Experience with cloud platforms (AWS, Azure, GCP), good to have development experience in AWS environments and preferably CI/CD pipelines

WORK SCHEDULE OR TRAVEL REQUIREMENTS

2- 11 PM IST, Mon - Fri. No travel.