Solution Architect - AI Application / Agentic AI
Location: Indore, India; Pune, India
Department: IT-Software
Experience: 8 - 10 Years
- Act as a technical solution architect in client-facing presales engagements, including discovery calls, solution walkthroughs, and proposal/SOW support.
- Translate business problems into AI-driven solution architectures, clearly articulating value, scope, assumptions, and implementation approach.
- Support account growth activities by identifying AI-led transformation opportunities and reusable solution patterns.
- Collaborate with sales and leadership teams on AI solution positioning, estimates, and technical risk assessments.
- Lead end-to-end solution architecture for AI applications and Agentic AI systems.
- Partner with AI application delivery teams to define:
- High-level and low-level architecture
- Integration patterns
- Data flow, orchestration, and scalability considerations
- Review and guide implementation to ensure adherence to architecture, security, performance, and design best practices.
- Provide architectural oversight across multiple AI initiatives as needed.
- Design and review Agentic AI systems, including multi-agent workflows, tool-using agents, planners, and autonomous/semi-autonomous agents.
- Apply Agentic AI design patterns, such as:
- Planner–Executor
- Tool-using / Function-calling agents
- Multi-agent collaboration
- Human-in-the-loop and guardrail-based workflows
- RAG-based and hybrid knowledge systems
- Evaluate and recommend latest AI models, frameworks, and orchestration tools based on use case fit, cost, performance, and governance.
- Contribute to AI R&D initiatives, PoCs, and technology exploration efforts.
- Design and build AI solution accelerators, reference architectures, and reusable components.
- Support internal capability building, including:
- Mentoring teams on AI and Agentic AI architecture
- Creating technical guidelines, best practices, and architectural playbooks
- Participating in internal skill development drives and knowledge-sharing sessions
- Strong understanding of modern AI/LLM ecosystems, including foundation models and fine-tuning concepts.
- Hands-on experience with AI application development, including:
- Prompt engineering and orchestration
- Retrieval-Augmented Generation (RAG)
- Agentic workflows and tool integrations
- Experience with Agent/Orchestration frameworks (e.g., Semantic Kernel, LangGraph, AutoGen, CrewAI, or equivalent).
- Awareness of AI governance, security, guardrails, and responsible AI considerations.
- Strong hands-on experience with:
- ASP.NET / ASP.NET Core
- C#
- SQL Server / Azure SQL
- Experience with Azure cloud services, especially in the context of AI solutions (preferred).
- Familiarity with web application architecture, APIs, authentication, and integration patterns.
- Solid understanding of software design principles and design patterns.
- Strong knowledge of enterprise and cloud architecture patterns.
- Experience designing scalable, secure, and maintainable systems.
- Ability to document architecture using diagrams, decision records, and technical notes.
- Proven ability to communicate complex technical concepts to non-technical stakeholders.
- Strong experience in client calls, workshops, and solution presentations.
- Ability to balance business needs, technical feasibility, and delivery constraints.
- 8–10 years of overall software engineering experience, with a significant portion in solution/technical architecture roles.
- Demonstrated experience delivering multiple production-grade projects as an architect.
- Prior experience working in technical presales or client-facing solution roles is mandatory.
- Exposure to AI-first or digital transformation programs is highly desirable.
- Hands-on exposure to agent frameworks and orchestration layers.
- Experience building AI accelerators, internal platforms, or reusable frameworks.
- Thought leadership through blogs, internal talks, whitepapers, or community contributions (nice to have).
- Strong architectural thinking with a practical, delivery-focused mindset.
- Curiosity and continuous learning in a rapidly evolving AI landscape.
- Ability to operate at the intersection of business, technology, and innovation.
- Collaborative leadership and mentoring orientation.
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