Artificial Intelligence Architect

Role: Artificial Intelligence engineer/ AI Solutions Architect

Location: Mumbai/Noida

Experience: 3-5 Years

Employment Type: Full Time, Permanent

Job Summary:

We are looking for an Artificial Intelligence engineer/ AI Solutions Architect to design, develop and implement intelligent AI agents that automate business processes and support real-world business operations.

The ideal candidate will have hands-on experience with Large Language Models (LLMs), prompt engineering, agent orchestration, API integrations and building AI-powered applications. You will work closely with AI, Development, Operations and Client Services teams to understand business requirements and translate them into scalable, reliable and well-governed AI solutions.

This is a hands-on technical role offering the opportunity to shape the design and implementation of AI agent systems from the ground up.

Key Responsibilities:

• Design, build and implement AI agents using LLMs, prompt engineering and orchestration frameworks.
• Develop agent workflows, define agent behaviour and establish clear rules, boundaries and execution logic.
• Integrate AI agents with APIs, internal tools, business applications and existing enterprise systems.
• Translate business and operational requirements into technical specifications, agent workflows and practical AI solutions.
• Identify opportunities to automate business processes and improve operational efficiency using Generative AI and agentic systems.
• Develop and optimise prompts, tool-calling mechanisms, workflows and multi-step agent interactions.
• Ensure AI agents operate reliably, securely and within defined governance and business rules.
• Troubleshoot agent performance issues, improve response quality and implement appropriate validation and error-handling mechanisms.
• Create and maintain technical documentation, Standard Operating Procedures (SOPs), implementation guides and reusable playbooks.
• Collaborate with cross-functional teams, including AI, Engineering, Operations and Client Services, to identify use cases and deliver AI solutions.
• Analyse existing platforms, systems and business processes to identify integration opportunities and potential challenges.
• Support continuous improvement of AI agent architecture, orchestration workflows and deployment practices.

Required Skills & Experience:

• 3-5 years of Hands-on experience building applications, workflows or tools using Large Language Models (LLMs) and Generative AI.
• Strong understanding of prompt engineering, LLM orchestration, AI agents and tool/function calling.
• Experience integrating AI applications with REST APIs, third-party tools and business systems.
• Ability to translate business requirements into technical designs, workflows and working solutions.
• Understanding of agent workflows, context management, error handling, validation and responsible AI practices.
• Experience creating technical documentation, SOPs, playbooks or implementation guides.
• Strong analytical thinking, problem-solving and system-design skills.
• Excellent communication skills, with the ability to engage technical and non-technical stakeholders.
• Ability to work in a fast-paced environment and manage evolving business requirements.

Preferred Skills:

• Experience with frameworks and tools such as LangChain, LangGraph, LlamaIndex, Semantic Kernel or similar LLM orchestration frameworks.
• Knowledge of Python and/or JavaScript/TypeScript.
• Experience with OpenAI APIs, Azure OpenAI or other LLM providers.
• Familiarity with function calling, structured outputs, RAG (Retrieval-Augmented Generation), vector databases and agent memory.
• Understanding of AI evaluation, guardrails, observability, monitoring and security controls.
• Experience with workflow automation platforms, enterprise applications or multi-agent systems.

Good to Have:

• Experience building and deploying LLM-powered applications or AI agents in commercial or production environments.
• Experience delivering AI solutions for real-world business use cases, beyond POCs, prototypes or academic projects.
• Exposure to enterprise AI deployments, including integration with existing business systems and operational workflows.