Senior GenAI Solutions Architect and AI Systems Lead

SRS Consulting Inc; New York (NY); On-site; USD 140,000 - 210,000; Full time

Job summary

SRS Consulting Inc. is seeking a Senior AI Engineer / GenAI Solutions Architect in NYC to design and deliver enterprise-grade AI, retrieval, and agent-based solutions.

You will shape architecture for scalable systems, ensure reliability, and drive production readiness across memory, context management, and observability. Ideal candidates combine strong software engineering with deep AI/LLM expertise, including design decisions, ADRs, and performance optimization for large data and complex

Qualifications

• Hands-on experience building GenAI, RAG, and agentic applications.
• Strong understanding of LLM architectures, prompting, model selection, and evaluation.
• Experience with multi-agent systems, tool calling, MCP, workflow orchestration, or similar patterns.
• Understanding of fine-tuning, embeddings, vector search, and retrieval architectures.

Responsibilities

• Design end-to-end AI, RAG, and agentic solutions for enterprise use cases.
• Evaluate architectural trade-offs and select appropriate patterns, models, and platforms.
• Create and defend Architecture Decision Records (ADRs) and technical designs.
• Identify risks, failure modes, scalability concerns, and optimisation opportunities.
• Build production-grade AI applications using LLMs, agents, workflows, and retrieval systems.
• Develop and integrate tools, APIs, vector databases, and knowledge systems.
• Implement memory, context management, guardrails, evaluation, and observability capabilities.
• Leverage AI-assisted coding tools while maintaining engineering ownership of the solution.

Skills

GenAI

RAG

Agents

Python

LLM architectures

Job description

SRS Consulting Inc. is seeking a Senior AI Engineer / GenAI Solutions Architect in NYC to design and deliver enterprise-grade AI, retrieval, and agent-based solutions.

You will shape architecture for scalable systems, ensure reliability, and drive production readiness across memory, context management, and observability. Ideal candidates combine strong software engineering with deep AI/LLM expertise, including design decisions, ADRs, and performance optimization for large data and complex