GenAI Solutions Architect / Sr AI Engineer
NYC Location
In-person interview - Must have
Role Overview
The ideal candidate combines strong software engineering skills with deep understanding of LLMs, RAG, agents, context management, evaluation, and scalability.
Key Responsibilities
Solution Design & Architecture
· 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.
AI Engineering & Development
· 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 (Claude Code, Cursor, GitHub Copilot, etc.) while maintaining engineering ownership of the solution.
Production Readiness
· Improve consistency, reliability, and performance of AI systems.
· Troubleshoot issues such as hallucinations, context bloat, latency, cost overruns, and output variability.
· Design monitoring, testing, evaluation, and governance frameworks for production systems.
· Optimize inference, retrieval, caching, and overall system performance.
Collaboration
· Work with product, architecture, data, and platform teams to define and deliver solutions.
· Translate business requirements into scalable technical architectures.
· Contribute to engineering standards, best practices, and reusable AI assets.
Required Skills & Experience
Core AI & LLM Engineering
· 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.
Architecture & System Thinking
· Ability to justify technology choices and architectural decisions.
· Experience designing solutions for enterprise-scale workloads and large data sets.
· Strong understanding of scalability, reliability, cost, performance, and maintainability trade-offs.
· Familiarity with Architecture Decision Records (ADR) and solution documentation.
Context & Memory Management
· Understanding of:
o Context management strategies
o Context compression and summarization
o Short-term and long-term memory patterns
o Retrieval optimisation
o Token and prompt efficiency
Engineering & Coding
· Strong programming skills in Python and modern software engineering practices.
· Experience with version control, testing, CI/CD, code reviews, and SDLC processes.
· Ability to read, review, optimise, and troubleshoot AI-generated code.
Optimization & Production Operations
· Understanding of:
o KV Cache
o Prompt caching
o Response caching
o Guardrails
o Evaluation frameworks
o Monitoring and observability
o Performance optimisation techniques
