GenAI Architect / Lead with Agentic AI

Generative AI Architect / Lead

Role & responsibilities:

• GenAI Solution Delivery

  : Design, build, and release enterprise-grade

  GenAI systems

  , including

  RAG pipelines, Agentic AI systems, multimodal LLMs, and foundation models

  .

• Agentic AI

  : Minimum

  612 months of experience

  in developing, deploying, and managing

  Agentic AI systems

  , with a clear understanding of

  tool usage, structured outputs, speculative decoding, AST-Code RAG, streaming, and sync/async processing

  .

• Fine-Tuning & PEFT

  : Hands-on expertise in

  PEFT methods (LoRA / QLoRA)

  for model fine-tuning and optimization.

• Embedding Models

  : Strong knowledge of embedding models, chunking strategies, and their limitations when used in RAG pipelines.

• Hands-on Coding

  : Write, test, and maintain clean, efficient, and scalable code in

  Python

  for building NLP and AI systems.

• Cloud & Deployment

  : Deep familiarity with

  Azure

  and proven experience in deploying LLMs for

  large-scale inference using LLMOps techniques and orchestration frameworks

  .

• Tech Stack Proficiency

  : Strong expertise in

  PyTorch, TensorFlow, Kubernetes, Docker, LlamaIndex, LangChain, and LangGraph

  .

• Innovation & Research

  : Stay updated on the latest advancements in

  AI agents, LLM architectures, and orchestration tools

  , experimenting with emerging techniques to enhance system performance.

• Communication

  : Strong interpersonal and communication skills, with the ability to design solutions and explain

  complex AI concepts

  to both technical and business stakeholders. Interact with CxOs executives. This is key for the role.

Preferred candidate profile:

• 612 years overall experience with strong balance of

  business & technical acumen

• 3+ years of

  GenAI development and deployment

  experience
• Min

  612 months Agentic AI

  development experience
• Strong

  Python development

  skills
• Proven experience in

  RAG pipelines, embeddings, and chunking strategies

• Expertise in

  LoRA / QLoRA fine-tuning

• Hands-on coding for

  NLP & LLMs

• Proficiency in

  PyTorch, TensorFlow, LangChain, LangGraph, LlamaIndex

• Deep familiarity with

  Azure Cloud, LLMOps, orchestration, and large-scale inference

• Knowledge of

  speculative decoding, AST-Code RAG, structured outputs, streaming & async processing

Preferred Qualifications

• Strong experience in

  enterprise-grade AI solution delivery

• Proven track record in

  building multi-modal LLM systems

• Excellent ability to bridge

  business goals with technical design