AI Architect

Job description

Location: Chennai (in-office)
Experience: 10-15 years
Employment Type: Full-time
Role Type: Architecture + Engineering

About the Role

Maarga Systems is looking for a hands-on AI Architect to provide strong technical and engineering across our AI Projects and initiatives.The ideal candidate will have a strong technology foundation in areas such as Java, Python, Data Engineering, Cloud, or Enterprise Architecture, combined with significant hands-on experience in AI/ML, Agentic AI, Multi Agents, Generative AI, MCP, Context Engineering, LangGraph, LLMs, and modern AI application architecture over the last 34 years.

The immediate need is for someone who can translate architecture into high-quality engineering execution, establish technical discipline, review implementations, mentor engineers, and demonstrate Maarga's AI capabilities to clients.

The person will work closely with senior leadership and engineering teams to make Maarga increasingly AI-native in its people, processes, engineering practices and delivery capabilities.

Key Responsibilities

1. AI Architecture & Technical

- Design and guide scalable, secure and production-ready AI/GenAI solutions.
- Translate business and client requirements into practical technical architectures.
- Partner with client architecture and technology leadership to shape technology choices, architecture patterns and engineering standards, and own how they are implemented consistently across projects.
- Provide technical direction across AI, GenAI, LLM, RAG, Agentic AI, data and cloud-based solutions, including the use of ontologies / knowledge models to ground AI in the client's business domain.
- Review solution designs and ensure architectural decisions are translated correctly into implementation.

2. Engineering Quality & Delivery Discipline

- Establish and enforce engineering standards and best practices for AI projects.
- Conduct architecture, code/design and implementation reviews at appropriate stages.
- Identify technical risks, design gaps, scalability issues and quality concerns early.
- Ensure that teams consistently follow agreed architecture and engineering practices.
- Work closely with delivery teams to improve technical quality, reliability, security and maintainability.

3. AI Engineering & GenAI

- Provide hands-on technical leadership across Generative AI and AI application development.
- Work with technologies such as LLMs, RAG, Agentic AI and multi-agent orchestration (e.g., LangGraph), MCP (Model Context Protocol), vector databases, context engineering, agent memory, evaluation frameworks (evals), model integration and AI application architectures.
- Guide teams in moving AI solutions from proof-of-concept to production, including harness engineering (the tooling, guardrails and test harnesses around agents) and human-in-the-loop (HITL) controls.
- Establish practical approaches for AI evaluation (evals), observability, security, cost management and performance.




- Stay current with rapidly evolving AI technologies and assess their practical applicability to Maarga's offerings.

4. Client & Presales Engagement

- Demonstrate Maarga's AI capabilities to clients and prospects.
- Participate in technical discussions, discovery workshops, solutioning and architecture sessions.
- Support proposals, PoCs and client presentations with strong technical depth.
- Help establish Maarga's credibility as an AI engineering and solutioning partner.
- Translate complex AI concepts into clear business and technical narratives for different stakeholders.

5. Team Development & AI Capability Building

- Provide day-to-day technical mentoring and guidance to the in-office engineering team.
- Identify capability gaps and help define learning and development priorities.
- Establish reusable architecture patterns, accelerators, frameworks and engineering practices.
- Help build internal AI capabilities and make Maarga increasingly AI-native.
- Coach engineers to improve their architecture, engineering and problem-solving skills.

6. Internal AI Transformation

- Help define how Maarga should build, deliver and govern AI solutions.
- Contribute to AI engineering standards, development processes, reusable assets and internal accelerators.
- Identify opportunities to improve productivity and quality through AI-enabled engineering practices.
- Work with leadership to build a sustainable AI capability rather than relying on individual projects or people.

Required Experience & Qualifications

- 10-15 years of overall technology/engineering experience.
- Strong foundational experience in one or more of:
- Java / Python
- Data Engineering
- Cloud Architecture
- Software Architecture
- Enterprise/Application Architecture

- Significant hands-on AI/ML/GenAI experience, particularly during the last 34 years.
- Strong understanding of modern AI application architecture and production implementation.
- Experience designing and delivering enterprise-grade AI/GenAI solutions.
- Strong understanding of cloud platforms (Azure preferred, including Azure AI Foundry / Azure OpenAI; AWS or GCP also considered).
- Hands-on experience with LLMs, RAG, Agentic AI, vector databases and AI application architectures; working knowledge of context engineering, agent memory, MCP and agent frameworks such as LangGraph.
- Strong software engineering and architecture fundamentals.
- Experience conducting technical/design reviews and establishing engineering standards.
- Robust client-facing communication and presentation skills.
- Ability to work hands-on with engineers rather than operating only at a conceptual or managerial level.

Preferred Experience





- Experience taking AI/GenAI solutions from PoC to production.
- Experience with enterprise clients and complex solution environments.
- Experience with harness engineering for agents, and with ontologies/knowledge graphs for domain grounding.
- Experience building reusable AI accelerators or platforms.
- Experience with AI evaluation (evals), observability, security, governance and human-in-the-loop (HITL) design.
- Experience in consulting, solution architecture, presales or technology advisory roles.
- Experience mentoring and developing engineering teams.
- Experience working across multiple technology stacks and business domains.

What We Are Looking For

The candidate should be able to:

Understand the architecture challenge the design guide the engineers review the implementation identify gaps fix the engineering approach explain the solution confidently to the client.

The role requires both technical depth and strong communication, with the role expected to be both client/project billable and focused on internal capability building, technical leadership and AI practice development.

What Success Looks Like

Within the first 612 months, the AI Architect should help Maarga:

- Improve the technical quality and consistency of AI deliverables.
- Establish practical AI engineering and architecture standards.
- Strengthen the capability of the existing engineering team.
- Increase the maturity of AI solutions from PoCs to production-ready implementations.
- Create reusable AI assets, patterns and accelerators.
- Strengthen client confidence in Maarga's AI capabilities.
- Contribute directly to revenue through delivery, solutioning and client engagements.
- Build a stronger and more sustainable internal AI capability.

Key Skills

AI Architecture • Agentic AI • Generative AI • LLMs • RAG • Context Engineering • Harness Engineering • LangGraph • MCP (Model Context Protocol) • Agent Memory • Human-in-the-Loop (HITL) • AI Evals • AI Observability • Ontology / Knowledge Graphs • Vector Databases • Azure AI Foundry / Azure OpenAI • Python / Java • Data Engineering • Cloud Architecture • Solution Architecture • Technical Presales

Why Join Maarga

- A CEO-backed mandate: Maarga is making itself AI-native across delivery, presales and internal operations this role sits at the centre of that transformation.

- Build the practice, not just a project: shape the AI engineering standards, reusable accelerators and team capability that Maargas AI business will run on.

- Direct leadership access: work closely with the CEO, with real influence on how Maarga builds, delivers and sells AI.

- Balanced role by design: roughly 5060% client work, with the rest protected for capability building — so your impact goes beyond a single client account.

- Real enterprise problems: AI solutions for clients across CPG/FMCG, Supply Chain, Manufacturing and Retail, from discovery and PoC through to production.

Compensation: Competitive and aligned with experience and depth of expertise.

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