Experienced AI Architect responsible for designing and delivering scalable, secure, and enterprise-grade AI solutions including agents, copilots, and intelligent automation systems. Works closely with cross-functional teams to translate business needs into robust technical architectures, establish design standards, and drive the adoption of modern AI technologies while ensuring performance, reliability, and governance across enterprise platforms.
Key Responsibilities :
Define the technical architecture and solution blueprint forAI-powered agents, copilots, and intelligent automation solutions across enterprise products and platforms.
Lead the design of scalable, secure, reusable, and enterprise-grade AI solutions that can be implemented by the AI Engineering team.
Establish architecture principles, design standards, reference patterns, and technical guardrails for AI applications and agentic systems.
Architect end-to-end AI solutions involving Large Language Models, retrieval-augmented generation, vector search, orchestration frameworks, enterprise APIs, workflow integration, and enterprise knowledge systems.
Work closely with product teams, business stakeholders, and engineering leadership to translate business requirements and product goals into robust technical architectures and implementation approaches.
Design architecture for AI agents supporting use cases such as intelligent assistants, recommendation engines, knowledge copilots, workflow automation, reporting intelligence, troubleshooting support, and decision-support applications.
Define solution patterns for prompt orchestration, memory design, context management, tool invocation, grounding, output validation, and observability.
Architect retrieval and knowledge access frameworks using enterprise documentation, support content, business knowledge repositories, operational data, and other structured and unstructured information sources.
Drive decisions on model access strategy, abstraction layers, vector storage, orchestration engines, caching approaches, and integration standards.
Review detailed technical designs prepared by engineers and provide architectural guidance to ensure alignment with defined standards and long-term technology direction.
Partner with platform, security, DevOps, QA, and enterprise architecture teams to ensure that AI solutions are production-ready, supportable, secure, and aligned with organizational standards.
Define non-functional architecture requirements for AI solutions including scalability, reliability, latency, resilience, cost efficiency, privacy, security, and governance.
Guide the team on AI engineering best practices, reusable frameworks, and architectural decisions without taking online management responsibility.
Evaluate new AI technologies, frameworks, and tools, and recommend adoption based on business fit, enterprise readiness, and architectural value.
Contribute to the long-term AI technology roadmap in collaboration with engineering leadership, product teams, and enterprise architecture stakeholders.
Personal Characteristics
Strong portfolio and excellent attitude.
Must be self-confident to work in a Team and to handle the responsibilities individually as well
Should be a good listener/ Can articulate well / Good Communication Skills
Ability to work with teams across organizational boundaries, different cultures and different time zones in a virtual environment
Delivery oriented and able to work under strict deadlines.
Mandatory Skills:
Bachelor’s degree in Computer Science, Information Technology, Artificial Intelligence, Data Science, Engineering, or a related field.
Minimum of 10 years of experience in software engineering and architecture, with at least 3 to 5 years of relevant experience in AI/ML, Generative AI, or enterprise AI solution architecture.
Strong experience in designing enterprise-scale software architecture and distributed systems.
Strong programming knowledge in Python and solid understanding of backend systems, APIs, integration patterns, and production-grade application design.
Hands-on experience with Large Language Models, Generative AI platforms, prompt engineering, embeddings, semantic search, vector databases, and retrieval-augmented generation.
Proven experience in architecting AI assistants, copilots, chatbots, or agent-based enterprise solutions.
Strong understanding of microservices, event-driven architecture, backend integration patterns, and enterprise application connectivity.
Ability to define architecture standards, reusable design patterns, and technical frameworks for engineering teams.
Strong understanding of AI evaluation, output validation, grounding techniques, hallucination reduction, and observability practices.
Good understanding of enterprise security, privacy, auditability, access control, and governance requirements for AI-enabled systems.
Experience with AWS, Azure, or GCP and architecture for scalable, cloud-native enterprise applications
Familiarity with containerization, CI/CD, monitoring, logging, and production architecture practices.
Strong architectural thinking, analytical ability, and problem-solving capability.
Good communication and collaboration skills, with the ability to influence engineers, architects, product managers, and technical stakeholders.
Experience working in Agile and modern product engineering environments.
Desirable Skills:
Experience in enterprise software, SaaS platforms, customer engagement products, analytics platforms, digital transformation initiatives, or workflow automation solutions.
Familiarity with AI orchestration and agent development frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, or similar tools.
Exposure to Java, Spring Boot, Node.js, Kafka, workflow engines, rules engines, and microservices-based enterprise platforms.
Experience with vector stores, caching layers, AI observability tools, and LLMOps platforms.
Understanding of recommendation systems, personalization, analytics platforms, and decision-support systems.
Familiarity with enterprise AI governance, responsible AI, and compliance-related architectural considerations.
Experience mentoring engineers and guiding technical design reviews without direct people management responsibility.
Understanding of enterprise platformization and reusable AI foundation architecture.
Software Architecture, AI Solution Architecture, Generative AI, Machine Learning, Large Language Models, Prompt Engineering, Retrieval Augmented Generation, Vector Databases, Semantic Search, Python, Backend Development, APIs, Distributed Systems, Microservices Architecture, Event Driven Architecture, Cloud Computing, AWS, Azure, GCP, Enterprise Integration, System Design, AI Agents, Chatbots, Copilots, Workflow Automation, Recommendation Systems, Knowledge Systems, Data Engineering, Embeddings, Context Management, Model Orchestration, LangChain, LangGraph, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, Node.js, Java, Spring Boot, Kafka, CI/CD, Containerization, DevOps, Monitoring, Logging, AI Observability, LLMOps, Security, Privacy, Governance, Access Control, Scalability, Reliability, Performance Optimization, Problem Solving, Agile Methodology, Technical Leadership, Stakeholder Management, Communication Skills