AI Engineer / GenAI Solutions Architect

Senior AI Engineer / GenAI Solutions Architect

NYC, NY

Duration: 12+ Months Contract

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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:
• Context management strategies
• Context compression and summarization
• Short-term and long-term memory patterns
• Retrieval optimisation
• 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:
• KV Cache
• Prompt caching
• Response caching
• Guardrails
• Evaluation frameworks
• Monitoring and observability
• Performance optimisation techniques

What Success Looks Like

A 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? xhyhwjd
• What happens when retrieval quality degrades at enterprise scale?

Ideal Candidate Profile

We 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