AI首席架构师

职位说明

The purpose of this role is to provide technical guidance and suggest improvements in development processes. Develop required software features, achieving timely delivery in compliance with the performance and quality standards of the company.

Role Overview

We are seeking an AI Tech Lead to engineer, scale, govern, and grow our AI delivery practice across GenAI, Agentic AI, and applied ML, within enterprise engagements.

This is a strictly hands

on, individual contributor and technical leadership role.

You will act as a core technical sentinel, helping grow frontier AI capabilities and engineering pillars.

You will be expected to come with expertise that help you actively contribute towards directing technical capability growth for the AI practice.

This role requires a builder’s mindset.

You must possess deep technical fluency in Deep Learning architectures and agentic AI, built upon a rock

solid foundation of software engineering.

You will lead technical execution, driving solution architectures, evaluation standards, and the technical bar for the AI engineering practice.

The expectation is technical 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, and enterprise platform deployments.

Key Responsibilities

Solution Engineering & Technical Execution

Lead the hands

on engineering for end

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.

tuned),retrieval design, memory optimization, and orchestration.

o Lead solutioning, support architecture for end

end AI solutions across GenAI,Agentic AI, multimodal, and applied ML use cases, with explicit trade

tuned), retrieval design, memory, andorchestration.

o Own the practice's reference architectures and solution design patterns formultimodal agentic systems, including planning, tool use, memory, grounding, andinter

agent communication (MCP, A2A).

o Conduct solution design reviews across concurrent client engagements

facilitatesubjective technical decisions and enable delivery excellence.

Multimodal Agentic Systems & SLM Design

Design and lead the build of multi

agent systems with reasoning, planning, tooluse, persistent memory, and grounded retrieval.

o Lead multimodal system design and solutions across text, vision, speech, and structured data, including ingestion, representation, and downstream agentreasoning.

o Establish patterns for SLM design and adoption — distillation, fine

tuning, quantization, and routing — to meet enterprise constraints on cost, latency, dataresidency, and on

prem/edge deploymento Define hybrid retrieval and knowledge architectures spanning vector, graph (KG),and NoSQL stores

lead KG

assisted retrieval, entity linking, and structuredgrounding

Eval, Guardrails & Production Quality

Establish evaluation as a first

class discipline: design eval frameworks, goldendatasets, regression suites, automated and human

loop evals, andobservability for agentic and generative systems.

o Define and enforce safety, guardrail, and hallucination

control standards acrossthe practice

lead red

teaming and adversarial testing for high

stakesdeployments.

o Set the bar for production readiness—reliability, latency, cost, monitoring, driftdetection, and incident response—for AI systems in regulated, enterprise

gradeenvironments.

o Lead GPU/accelerator ops, model serving, and lifecycle automation fordeployment across cloud hyper

scalers, on

prem, and edge

Technical Leadership & Capability Pillars

Act as a technical sentinel for the AI practice, mentoring engineers throughrigorous code and architecture reviews to ensure permanent capability buildingrather than temporary crisis management.o Establish and enforce AI in SDLC frameworks on delivery projects

Cross-functional Leadership & Delivery

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.

Must Have

Deep practical grounding in neural networks,Transformers, predictive modeling, embeddings, vector search; CV, NLP, and timeseries exposure.

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.

Programming & Engineering: Python (advanced), SQL; strong API and backend engineering in FastAPI/Flask/Django; production-grade software practices

Cloud & Data Engineering: Kafka, Spark/Flink, Hadoop, MongoDB, and otherNoSQL/graph/vector stores. Deep experience with AWS, Azure, or GCP

Math Foundations: Linear algebra, probability, statistics, optimization. Experience Requirements

Minimum 8+ years of total hands-on software development and engineering experience, with a proven track record of reliably deploying solutions on enterprise platforms.

3+ years of deep, hands-on experience building and deploying Deep Learning

and AI systems in production (within the total 8 years of experience)

Demonstrable hands-on work in GenAI and/or Agentic AI—beyond basic APIwrappers and simple RAG—including multi-agent systems, custom fine-tuning, orSLM-based deployment.

Good to have

Experience with commerce cloud ecosystems (Salesforce andAdobe).

Location

DGS India - Bengaluru - Manyata N1 Block

Merkle

Time Type

Full time

Contract Type

Permanent