AI Lead Architect

Job Description:

Key Responsibilities

Solution Engineering & Technical Execution:

o Lead the hands-on engineering for end-to-end AI solutions across Deep Learning

GenAI Agentic AI and multimodal use cases.

o Apply rigorous fail fast logic to all AI project management. Quickly identify

evaluate and disqualify unviable AI use cases based on technical feasibility effort

cost and risk early in the cycle.

o Perform explicit trade-off analysis on model class (frontier vs. SLM vs. fine-tuned)

retrieval design memory optimization and orchestration.

o 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.

o Own the practices reference architectures and solution design patterns for

multimodal agentic systems including planning tool use memory grounding and

inter-agent communication (MCP A2A).

o Conduct solution design reviews across concurrent client engagements; facilitate

subjective technical decisions and enable delivery excellence.

Multimodal Agentic Systems & SLM Design:

o Design and lead the build of multi-agent systems with reasoning planning tool

use persistent memory and grounded retrieval.

o Lead multimodal system design and solutions across text vision speech and

structured data including ingestion representation and downstream agent

reasoning.

o 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

o 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:

o 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.

o Define and enforce safety guardrail and hallucination-control standards across

the practice; lead red-teaming and adversarial testing for high-stakes

deployments.

o Set the bar for production readinessreliability latency cost monitoring drift

detection and incident responsefor AI systems in regulated enterprise-grade

environments.

o Lead GPU/accelerator ops model serving and lifecycle automation for

deployment across cloud hyper-scalers on-prem and edge.

Technical Leadership & Capability Pillars:

o Act as a technical sentinel for the AI practice mentoring engineers through

rigorous code and architecture reviews to ensure permanent capability building

rather than temporary crisis management.

o Establish and enforce AI in SDLC frameworks on delivery projects.

Cross-functional Leadership & Delivery

o Engage with client and stakeholder leadership on architecture feasibility and risk;

communicate technical direction clearly to non-technical audiences.

o Support pre-sales and solutioning for new GenAI and Agentic AI opportunities

including effort estimation architectural framing and capability storytelling.

Technical Skills

• Deep Learning & Machine Learning: Strong hands-on experience with neural networks Transformers predictive modeling embeddings and vector search.
• Generative AI: Hands-on experience with LLMs/SLMs RAG/Agentic RAG agents prompt engineering grounding multimodal architectures and production GenAI solutions.
• Fine-tuning: Practical experience with techniques such as SFT LoRA/QLoRA RLHF/RLAIF distillation and/or quantization.
• Agentic AI: Hands-on experience with multi-agent orchestration planning tool use memory and agentic workflows. Experience with frameworks such as LangGraph LlamaIndex or AutoGen.
• Programming & Engineering: Advanced Python SQL strong API/backend engineering experience using FastAPI Flask Django or equivalent frameworks.
• Production Engineering: Proven experience designing developing testing and deploying AI/ML solutions in enterprise production environments.
• Cloud: Strong hands-on experience with at least one major cloud platform AWS Azure or GCP.
• Data/Storage: Experience with databases and data platforms such as MongoDB NoSQL vector databases graph databases or equivalent.
• Experience: Minimum 8 years of total hands-on software development/engineering experience.
• AI Experience: Minimum 3 years of hands-on experience building and deploying Deep Learning/AI systems in production.
• GenAI/Agentic AI: Demonstrable hands-on experience beyond basic API integrations or simple RAG implementations such as multi-agent systems custom fine-tuning advanced RAG or SLM deployments.
• Work Location: Willingness to work from the Pune office at least 3 days per week.

Good to have:

Experience with commerce cloud ecosystems (Salesforce and

Adobe).

Attitude & Mindset

Equipped with a builders hands and a highly pragmatic approach to enterprise AI.

Prioritizes technical validation pragmatic domain expertise and rigorous testing over

AI hype.

Open and flexible toward a hybrid work structure with no less than 3 days work from

the office in Pune ensuring regular connection and cross-project knowledge.

Location:

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