AI Architect

Understand the current legacy system and architect, scale
Agentic AI systems capable of multi-step reasoning and autonomous action for
application modernization.

Define reference architectures for container-based app
hosting, memory management (vector DBs), and robust data pipelines.

Lead the product vision for reusable AI modules, including
RAG (Retrieval-Augmented Generation), hybrid ML/LLM systems, and standardized
evaluation frameworks.

Evaluate and select the LLM stack (models, orchestration
frameworks, and observability tools) based on cost, latency, and performance.

Standardize advanced prompting patterns, including
chain-of-thought, few-shot templates, and automated chunking strategies to
optimize for accuracy and cost.

Drive team productivity by implementing and championing
AI-assisted coding workflows (e.g., GitHub Copilot, Claude Code) to accelerate
the Development & migration project.

Embed LLM workflows into existing enterprise systems,
ensuring seamless interoperability and high-performance output.

Collaborate with security teams to enforce AI safety, data
privacy, and ethical AI practices.

Recruit, coach, and lead a high-performing GenAI engineering
team, fostering a culture of continuous learning.

Serve as the primary SME for post-sale customer engagements
and internal leadership, translating complex AI concepts into actionable
business strategies10%2B years of experience in AI/ML engineering with 1%2B years
in agentic AI and LLM systems

Hands-on development experience using Github Copilot, Claude
Code for rapid AI prototyping and implementation

Expertise in any of the cloud platform (AWS / Azure / Google Cloud Platform)

Strong proficiency in LangGraph, LangMem, and agentic
workflow development

Experience with reasoning, instruct, safety & embedding
models

Extensive experience in LLM evaluation and testing
(LangGraph evals, Agent evals, DeepEval, prompt testing frameworks)