Job Title : AI Architect - Agentic Systems & Data Context

Location: Chennai

Job Type: Full-Time

Experience Level: 8+ years

Role Overview

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.

Key Responsibilities :

- Strategic AI System Design: Translate complex business requirements into coherent, scalable, and secure technical blueprints for end-to-end AI solutions.

- 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.

- 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.

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

- 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.

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

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

Required Qualifications

- 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.

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

Core Architecture Skills:

- Proven experience designing scalable and resilient systems on cloud platforms (AWS, Azure, or GCP).

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

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

Data & Context Architecture Skills:

- Demonstrable experience designing and implementing Retrieval-Augmented Generation (RAG) architectures to ground AI responses in enterprise data.

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

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

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

Agentic AI & ML Skills:

- Experience designing agentic AI architectures, including single-agent and multi-agent systems.

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

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

- Architectural knowledge of the end-to-end MLOps lifecycle.

Leadership & Communication Skills :

- Exceptional ability to translate business strategy into a technical roadmap.

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

- Proven experience in mentoring and providing technical leadership to development teams.

Preferred Qualifications

- Masters or Ph.D. in Computer Science, Artificial Intelligence, or a related field.

- Cloud certifications (e.g., AWS Certified Solutions Architect, Azure Solutions Architect Expert).

- Published research or active contributions to open-source AI/ML projects.