Senior AI Engineer / GenAI Solutions Architect

Senior AI Engineer / GenAI Solutions ArchitectNYC, NY12+ Months

Remember to check your CV before applying Also, ensure you read through all the requirements related to this role.
Role OverviewThe ideal candidate combines strong software engineering skills with deep understanding of LLMs, RAG, agents, context management, evaluation, and scalability.
Key ResponsibilitiesSolution Design & ArchitectureDesign 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 & DevelopmentBuild 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 ReadinessImprove 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.CollaborationWork 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 & ExperienceCore AI & LLM EngineeringHands-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 ThinkingAbility 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 ManagementUnderstanding of:Context management strategiesContext compression and summarizationShort-term and long-term memory patternsRetrieval optimisationToken and prompt efficiencyEngineering & CodingStrong 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 OperationsUnderstanding of:KV CachePrompt cachingResponse cachingGuardrailsEvaluation frameworksMonitoring and observabilityPerformance optimisation techniques
What Success Looks LikeA successful candidate can confidently answer questions such as:When should RAG be used instead of fine-tuning?Why choose a multi-agent architecture versus a workflow?How would you manage a million-token context?Why is an AI agent producing inconsistent outputs?Where should guardrails be implemented?Would KV caching help in this scenario? Why and where? xzbcwsv What happens when retrieval quality degrades at enterprise scale?
Ideal Candidate ProfileWe are looking for someone who is: A strong engineer first Experienced with production AI systems Comfortable with ambiguity and problem solving Able to reason about architectural trade-offs Curious, pragmatic, and continuously learning Capable of designing solutions, not just building demos