AI Solutions Architect

Keka Technologies Private Limited; Pune District; On-site; INR 1,200,000 - 1,500,000; Full time

Job summary

Keka Technologies Private Limited is seeking a seasoned AI Solutions Architect to design and deliver AI solutions tailored to client needs. The role involves collaborating with organizational teams to drive AI adoption and mentoring junior engineers.

The ideal candidate should have over 9 years of experience in software engineering, with a strong emphasis on agentic AI delivery. Proficiency in LangChain and automated solutions is crucial. This position offers significant influence over AI strategy across internal and client-facing projects.

Qualifications

• 9–12 years of overall experience in software engineering or technology consulting.
• Minimum 3 years in applied AI/GenAI engineering or architecture in production.

Responsibilities

• Design and deliver AI solutions for clients and internal teams.
• Mentor junior AI engineers and collaborate on architecture standards.
• Support AI adoption and operationalization within client organizations.

Skills

Agentic AI delivery experience

LangChain

AI integrations

Automation pipelines

Microservices architecture

Tools

Azure AI Foundry

Power Automate

Job description

Responsibilities

• Translate client and internal business requirements into feasibility-validated, buildable AI solutions — delivered, not just proposed.
• Design and deliver end-to-end AI solutions, including agentic workflows, AI integrations, automation pipelines, and LLM-powered applications for internal and external customers.
• Package delivered solutions into documented, reusable assets that can be applied across engagements.
• Engage directly with US-based clients in discovery sessions, workshops, and solution reviews as the technical AI subject matter expert.

Agentic AI, Automation & Workflow

• Design and build multi-agent AI systems using LangChain, LangGraph, CrewAI, or AutoGen.
• Architect MCP (Model Context Protocol) integrations connecting AI agents to enterprise tools, data sources, and business workflows.
• Build agentic automation that executes real, multi-step business processes with appropriate human-in-the-loop oversight.
• Apply prompt engineering, RAG pipelines, and vector database integrations to deliver accurate, context-aware AI solutions at production scale.

AI Integration & Application Modernization

• Design AI-assisted integration solutions that connect disparate systems, data sources, and platforms — platform-agnostic, applicable across diverse client technology environments.
• Apply AI approaches to accelerate application modernization, legacy migration, and systems integration across client technology stacks.
• Build reusable AI accelerators for code analysis, migration planning, and integration mapping that can be deployed across engagements.

Organizational AI Adoption & Enablement

• Support internal teams and external clients in adopting and operationalizing AI, from readiness assessment through practical implementation and change enablement.
• Design AI guardrails and governance practices, including data handling, human-in-the-loop controls, and responsible AI use that are appropriate for client-facing agentic systems.
• Build AI solutions that improve operational efficiency for both client organizations and Integrative's own internal teams and functions.
• Develop practical adoption frameworks, runbooks, and enablement materials that help non-AI practitioners use and sustain delivered solutions.
• Recruit and mentor junior AI engineers; collaborate with Integrative's enterprise architecture group to ensure AI solutions align with broader technology standards.
• Continuously monitor emerging AI tools, frameworks, and platform releases; evaluate applicability and bring relevant recommendations forward.

Agentic AI & Orchestration

• LangChain and LangGraph — hands‑on production experience required. MCP (Model Context Protocol) design and implementation required.
• Multi‑agent frameworks (CrewAI, AutoGen, or equivalent) strongly preferred. Tool calling, memory management, context and state handling, RAG pipeline architecture.
• API‑first and microservices architecture; enterprise integration patterns; prompt engineering at scale.
• Azure AI Foundry, Azure OpenAI Service, Copilot Studio, Power Automate with AI Builder, Azure DevOps / CI‑CD.

Delivery & Communication

• Demonstrated ability to deliver AI solutions that generate measurable business outcomes — for both internal teams and external clients.
• Clear solution documentation and business communication; fluent written and spoken English; confident in client‑facing settings.
• Experience supporting organizational AI adoption — helping teams understand, use, and sustain AI solutions.
• Distributed (onshore/offshore) delivery experience preferred.

Qualifications & Experience

We are looking for demonstrated applied AI architecture capability — not simply total years in the workforce. Strong, recent, hands‑on agentic AI delivery experience is preferred over a longer but more generic technology career.

• 9–12 years of overall professional experience in software engineering, solution architecture, or technology consulting.
• Minimum 3 years in applied AI/GenAI engineering or architecture, in production environments — not academic or R&D‑only work.