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