Senior AI/Agentic Solution Architect & Engineer

About the Role

We are seeking a highly skilled Senior AI/Agentic Solution Architect & Engineer who combines deep architectural thinking with hands-on engineering excellence in agentic AI systems. This role demands someone who can design enterprise-grade multi-agent architectures, lead client-facing technical discussions, and personally build and deploy production-quality agentic applications.

You will be responsible for the end-to-end lifecycle of agentic AI solutions from discovery and architecture through to implementation, production deployment, and continuous optimisation. You will work directly with clients and cross-functional teams to translate complex business problems into intelligent systems that reason, act, and learn.

Experience Requirements

Minimum 10 years of skilled experience in software engineering and solution/system architecture

Minimum 3 years of hands-on experience building and deploying agentic AI applications and workflows using LLMs (Claude, OpenAI GPT series, Google Gemini, or similar)

Proven track record of taking AI/LLM systems from prototype to production at enterprise scale

Bachelor's or Master's degree in Computer Science, Engineering, Artificial Intelligence, or a related field

Technical Skills

AI & Agentic Systems

Deep expertise in designing, developing, and deploying LLM-based agentic applications and complex AI workflows

Hands-on experience with agentic frameworks and orchestration tools — LangChain, LangGraph, Crew AI, OpenAI Agent SDK, AutoGen, or similar

Strong understanding of agentic concepts: planning, memory, tool usage, reasoning chains, multi-agent coordination, human-in-the-loop workflows, and failure handling

Practical experience building Retrieval-Augmented Generation (RAG) systems using embeddings, vector databases, and semantic search

Advanced proficiency in Prompt Engineering and Context Engineering — designing effective prompts, managing context windows, structured outputs, and chain-of-thought reasoning

Experience with Model Context Protocol (MCP) servers and tool integration patterns

Understanding of agent observability, guardrails, governance patterns, and safety mechanisms

Ability to evaluate and integrate emerging AI research, models,



and protocols into production systems

Software Engineering & Architecture

Strong proficiency in Python — production-quality development, not just prototyping

Deep experience with API design and development (REST, gRPC, event-driven architectures) and integrating external services

Solid backend engineering fundamentals — distributed systems, microservices, data pipelines, message queues

Experience in automation engineering — CI/CD pipelines, infrastructure-as-code, automated testing strategies

Deep insight into the end-to-end software development lifecycle — from requirements through to production operations

Production deployment and operations experience — performance tuning, cost optimisation, monitoring, and iteration

Familiarity with cloud platforms (AWS, Azure, or GCP) and containerisation/orchestration (Docker, Kubernetes)

Solid understanding of version control (Git), testing strategies, and software development best practices

Experience with Snowflake is a plus

Testing & Evaluation

Ability to implement testing, evaluation, and monitoring strategies for agentic systems

Experience measuring and ensuring effective reasoning, tool usage, reliability, and safety in production AI systems

Soft Skills & Leadership

Communication — Ability to articulate complex technical concepts clearly to both technical and non-technical audiences; strong written and verbal skills

Stakeholder Management — Proven ability to manage expectations, build trust, and maintain alignment across client leadership, delivery teams, and internal stakeholders

Technical Leadership — Ability to lead architecture discussions, design reviews, and problem-solving sessions with clients and engineering teams

Client-Facing Delivery — Comfortable presenting to C-level and senior leadership; can translate business problems into technical solutions and vice versa





Diligence in Reporting — Strong discipline in status reporting, program updates, risk/issue tracking, and delivery governance; keeps stakeholders informed proactively

Mentorship — Ability to mentor engineers, share knowledge, and elevate the team's capability in AI/agentic development

Problem-Solving — Excellent analytical skills with the ability to work independently and drive solutions in ambiguous environments

Key Responsibilities

Define and own the technical architecture for agentic AI solutions across client engagements

Design, develop, and deploy LLM-based agentic applications and complex multi-agent workflows end-to-end

Build and optimise RAG systems, prompt strategies, and interaction patterns for production use

Integrate tools, APIs, data sources, and MCP servers to enhance agent capabilities and context

Deploy, operate, and iterate on AI systems in production — including performance tuning and cost optimisation

Lead technical discovery, solutioning workshops, and architecture reviews with clients

Implement testing, evaluation, and monitoring strategies to ensure system reliability and safety

Collaborate with product managers, designers, and engineers to translate requirements into technical solutions

Establish best practices for prompt engineering, context management, and agent design within the organisation

Contribute to the technical roadmap, proposals, and pre-sales discussions

Maintain clear and diligent reporting on delivery progress, technical risks, and architectural decisions

Mentor junior engineers and drive knowledge sharing across the team

Stay current with the latest research in generative AI, LLMs, agentic systems, and emerging protocols

Preferred Qualifications

Experience in enterprise domains such as banking/financial services, insurance, or healthcare

Familiarity with AI governance frameworks and responsible AI practices

Contributions to open-source projects or published thought leadership in AI/agentic systems

Experience leading or contributing to operating model transformations involving AI

Background in designing agent catalogues, governance-as-platform patterns, and self-healing pipelines

📌 Solution Architect - Gen AI / Agentic AI (Bengaluru)
🏢 Ascendion Engineering
📍 Bengaluru