Data Architect Agentic AI

Job description

<p><p><b>Job Title : AI Architect - Agentic Systems & Data Context</b><br/><br/><b>Location: Chennai</b><br/><br/><b>Job Type: Full-Time</b><br/><br/><b>Experience Level: 8+ years</b><br/><br/><b>Role Overview</b><br/><br/>As our AI Architect, you will be the chief designer of our intelligent, autonomous systems.

You will establish the technical vision and architectural blueprint for Kripya's next-generation agentic AI solutions that power our hyper-automation platform.

You will bridge the gap between our ambitious business goals and our engineering teams, making high-stakes technology decisions that will define our scalability, security, and competitive edge for years to come.

This is a leadership role for a strategic thinker passionate about building robust, enterprise-grade AI solutions that solve complex data and context management challenges.

<br/><br/><b>Key Responsibilities :</b><br/><br/>- Strategic AI System Design: Translate complex business requirements into coherent, scalable, and secure technical blueprints for end-to-end AI solutions.

<br/><br/>- Agentic AI Architecture: Design and oversee the implementation of multi-agent AI architectures, defining agent roles, communication protocols, and orchestration workflows to solve complex business problems autonomously.

<br/><br/>- Data Context and Retrieval Architecture: Architect scalable solutions for managing AI context windows when processing large volumes of enterprise data, utilizing patterns like Retrieval-Augmented Generation (RAG) and knowledge graphs.

<br/><br/>- Technology Governance & Selection: Evaluate and select the optimal technologies, platforms, and frameworks for building agentic AI, including LLMs, vector databases, and data processing tools.

<br/><br/>- Ethical & Secure AI: Define and enforce governance frameworks for the ethical, secure, and compliant deployment of autonomous AI systems, addressing potential risks and ensuring model interpretability.

<br/><br/>- Technical Leadership & Mentorship: Provide architectural oversight and guidance to AI engineering teams, ensuring implementation aligns with the strategic technical vision.

<br/><br/>- Stakeholder Communication: Clearly articulate complex AI concepts, strategies, and architectural decisions to technical teams and non-technical business leaders to ensure alignment.

<br/><br/><b>Required Qualifications</b><br/><br/>- Experience: A minimum of 8 years in software engineering and system architecture, with a proven track record of designing and deploying large-scale AI/ML solutions.

<br/><br/>- Education: Bachelors degree in computer science, AI, or a related technical field.

<br/><br/><b>Core Architecture Skills:</b><br/><br/>- Proven experience designing scalable and resilient systems on cloud platforms (AWS, Azure, or GCP).

<br/><br/>- Deep architectural knowledge of microservices, event-driven systems, and containerization technologies (Docker, Kubernetes).

<br/><br/>- Proficiency in designing data storage patterns for AI systems using various SQL and NoSQL databases.

<br/><br/><b>Data & Context Architecture Skills:</b><br/><br/>- Demonstrable experience designing and implementing Retrieval-Augmented Generation (RAG) architectures to ground AI responses in enterprise data.

<br/><br/>- Proficiency in context engineering techniques for optimizing LLM performance with large datasets (e.g., context selection, summarization, and management).

<br/><br/>- Experience with knowledge graphs or semantic layers to provide structured, machine-readable context to AI systems.

<br/><br/>- Strong understanding of big data technologies (e.g., Spark, Kafka) for processing data at scale.

<br/><br/><b>Agentic AI & ML Skills:</b><br/><br/>- Experience designing agentic AI architectures, including single-agent and multi-agent systems.

<br/><br/>- Strong architectural understanding of LLMs and their application as reasoning engines in autonomous systems.

<br/><br/>- Familiarity with the principles of agentic frameworks (e.g., LangChain, AutoGen).

<br/><br/>- Architectural knowledge of the end-to-end MLOps lifecycle.

<br/><br/><b>Leadership & Communication Skills :</b><br/><br/>- Exceptional ability to translate business strategy into a technical roadmap.

<br/><br/>- Excellent communication skills, with the ability to influence and build consensus with technical and non-technical stakeholders.

<br/><br/>- Proven experience in mentoring and providing technical leadership to development teams.

<br/><br/><b>Preferred Qualifications</b><br/><br/>- Masters or Ph.D. in Computer Science, Artificial Intelligence, or a related field.

<br/><br/>- Cloud certifications (e.g., AWS Certified Solutions Architect, Azure Solutions Architect Expert).

<br/><br/>- Published research or active contributions to open-source AI/ML projects.</p><br/></p> (ref:hirist.tech)

Required Skill Profession

Computer Occupations