Lead Architect

dentsuaegis; Karnataka; Hybrid; INR 2,800,000 - 5,500,000; Full time

Job summary

DentsuAegis seeks an AI Solutions Lead/Associate Architect to architect, govern, and grow the AI delivery practice. The role combines hands-on design with strategic leadership, steering solutions for multiple pods and shaping technical standards.

Hybrid work enabling collaboration with DX and engineering leaders is expected. The candidate should bring fluency in GenAI and agentic AI, a strong ML/DL foundation, and the ability to raise the technical bar across the practice while guiding mature

Qualifications

• Proficiency in Python and SQL for AI solution engineering.
• Experience with API design and backend services in production environments.
• Deep understanding of GenAI, agentic AI, and multimodal architectures.

Responsibilities

• Translate client problems into AI solution roadmaps and designs.
• Lead end-to-end AI solutions across GenAI, Agentic AI, and multimodal use cases.
• Define evaluation, safety, and production-readiness standards for enterprise deployments.

Skills

Python

SQL

API & Backend

GenAI

Agentic AI

Multimodal AI

Tools

LangGraph

LlamaIndex

AutoGen

Job description

Job Description:

AI Solutions Lead/AI (Associate) Architect

Role Overview

We are seeking an AI Solutions Lead to architect, govern, and grow our AI delivery practice across GenAI, Agentic AI, and applied ML engagements. This is a hands-on and hybrid role that includes architecting AI solutions and shaping the growth of the AI practice. This role is suited for someone who has earned technical fluency with GenAI and agentic AI on top of a strong foundation in classical ML and deep learning - and who is now ready to set the technical direction for a growing team. The role is a hybrid lead position: leading solutions for multiple AI delivery pods, partnering with engineering and DX leadership, and owning the craft of the practice - driving solution architectures, evaluation standards, reusable components, and the technical bar for the AI delivery team. The expectation is depth and breadth showcased across the portfolio of AI work done so far, preferably in multimodal agentic systems, SLM design, ML and DL solutions, and enterprise deployments, while consistently raising the standards at which the team operates.

Key Responsibilities

Solution Architecture & Technical Direction

• Translate business problems from clients into staged, defensible AI solution roadmaps working with business leaders through pre-sales and project delivery cycles.
• Lead solutioning, support architecture for end-to-end AI solutions across GenAI, Agentic AI, multimodal, and applied ML use cases, with explicit trade-off analysis on model class (frontier vs. SLM vs. fine-tuned), retrieval design, memory, and orchestration.
• Own the practice's reference architectures and solution design patterns for multimodal agentic systems, including planning, tool use, memory, grounding, and inter-agent communication (MCP, A2A).
• Conduct solution design reviews across concurrent client engagements; facilitate subjective technical decisions and enable delivery excellence.

Multimodal Agentic Systems & SLM Design

• Design and lead the build of multi-agent systems with reasoning, planning, tool use, persistent memory, and grounded retrieval.
• Guide multimodal system design across text, vision, speech, and structured data, including ingestion, representation, and downstream agent reasoning.
• Establish patterns for SLM design and adoption - distillation, fine-tuning, quantization, and routing - to meet enterprise constraints on cost, latency, data residency, and on-prem/edge deployment.
• Define hybrid retrieval and knowledge architectures spanning vector, graph (KG), and NoSQL stores; lead KG-assisted retrieval, entity linking, and structured grounding.

Eval, Guardrails & Production Quality

• Establish evaluation as a first-class discipline: design eval frameworks, golden datasets, regression suites, automated and human-in-the-loop evals, and observability for agentic and generative systems.
• Define and enforce safety, guardrail, and hallucination-control standards across the practice; lead red-teaming and adversarial testing for high-stakes deployments.
• Set the bar for production readiness - reliability, latency, cost, monitoring, drift detection, and incident response - for AI systems in regulated, enterprise-grade environments.
• Drive enterprise deployment best practices across cloud hyper-scalers, on-prem, and edge, including GPU/accelerator ops, model serving, and lifecycle automation.

Practice Building & Technical Mentorship

• Shape the practice's capability roadmap: which techniques to invest in, which to retire, and how the team stays at the leading edge of GenAI and agentic AI.
• Mentor AI Engineers and Lead AI Engineers; run technical reviews, pairing sessions, and internal knowledge exchange on agentic, multimodal, and SLM topics.
• Set the technical hiring bar; lead architecture and senior engineering interviews and calibrate the team's evaluation standards.
• Establish and promote AI in SDLC frameworks on delivery projects.

Cross-functional Leadership & Delivery

• Partner with engineering, data science, product, and DX leadership on delivery and acceleration initiatives.
• Engage with client and stakeholder leadership on architecture, feasibility, and risk; communicate technical direction clearly to non-technical audiences.
• Support pre-sales and solutioning for new GenAI and Agentic AI opportunities, including effort estimation, architectural framing, and capability storytelling.

Required Technical Skills

• Programming & Engineering: Python (advanced), SQL; strong API and backend engineering in FastAPI/Flask/Django; production-grade software practices.
• Generative AI: LLMs and SLMs, RAG/Agentic RAG, multimodal architectures, agents, prompt engineering, grounding, knowledge graphs, fine-tuning (SFT, LoRA/QLoRA, RLHF/RLAIF), distillation, and quantization.
• Agentic AI: Multi-agent orchestration, planning, tool use, persistent memory, MCP and A2A patterns; frameworks such as LangGraph, LlamaIndex, AutoGen.
• Eval & Safety: Eval framework design, golden datasets, automated and human