Senior AI Engineer / GenAI Solutions Architect NYC, NY Hybrid Onsite
12+ Months 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
