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Applied AI Engineering Lead
Thresh Consulting
40 minutes ago
Full-time
On-site
New York City, New York, United States
APPLIED AI ENGINEERING LEAD
Thresh Consulting — Strategy in Action for the AI Era Salary: $190,000 – $235,000 + up to 25% performance-based bonus Thresh Consulting is a modern digital consulting firm built for the AI era—where strategy, execution, and craft come together to deliver real outcomes with speed. Our specialty is helping organizations design, build, and scale digital products that create exceptional customer experiences and drive value.
We value clarity over complexity, quality over shortcuts, and learning as a core part of growth.
The Applied AI Engineering Lead turns ambition into production systems that move a client's business. You'll own the technical direction of our AI engagements: deciding which problems are worth solving with AI, architecting the solution, and leading the team that ships it. Your depth is in applied LLM engineering: agents, tool use, retrieval, evaluation, and the guardrails that make AI safe to put in front of customers. You're fluent enough across front end, APIs, data, and cloud to build an experience end-to-end yourself when it counts.
You'll spend about 70% of your time building and 30% leading. You'll guide a team of 2-4 engineers and serve as a primary partner to client engineering leaders. You'll also work with Thresh's practice leads to scope and shape new AI work.
Set technical direction for AI engagements
Partner with clients to identify and prioritize high-value AI use cases, and be clear about what's ready for production versus what's still a demo. Make build, buy, prompt, or fine-tune calls based on cost, latency, quality, and risk, and explain those calls to client leadership. Shape scope, estimates, and technical approach for new AI engagements alongside Thresh's practice and sales leads.
Build AI-powered, customer-facing experiences
Build conversational shopping experiences where customers discover, compare, and buy through natural language, from first question to completed order. Bring real business logic into the conversation: live pricing, inventory, promotions, personalization, and customer account context.
Build for a client's own site and app and for third-party AI assistants like ChatGPT and Gemini, all from one shared foundation. Build and extend MCP servers and tools that give models safe, structured access to catalog, pricing, cart, and order capabilities. Prepare commerce systems for AI agents that shop on a customer's behalf, with structured product data, agent-ready checkout, and clear rules for what an agent can and can't do. Integrate with enterprise commerce, catalog, order, and identity systems that were never designed for AI. Build retrieval and knowledge systems (RAG) over client content, catalogs, and documentation, and tune them for accuracy.
Make AI trustworthy in production
Prepare work for the legal, privacy, security, and brand reviews that every transactional AI experience goes through. Monitor model quality, drift, and spend after launch, and plan for model upgrades and vendor changes without breaking the experience.
Make AI coding agents (Claude Code, Cursor) your default way of working. Prototype conversational features in days with mock data, so clients can see the experience before the real integrations land. Mentor engineers through code review, pairing, and technical guidance, across the full applied AI stack, from prompting and evals to production architecture. Partner with product managers and designers on conversation design, scope, technical risk, and estimates. Define the components, patterns, and standards that become reusable assets across Thresh's AI work, including reference architectures and starter kits. Help build Thresh's applied AI capability through hiring input, technical interviews, and internal enablement. Represent Thresh's AI point of view with clients and platform partners through workshops, technical content, and pre-sales conversations.
Own AI solutions from discovery through production deployment. Explain progress and tradeoffs clearly to technical and non-technical stakeholders, including what AI can and can't reliably do. Hold the quality bar when deadlines tighten and requirements shift.
7+ years building and shipping production software, including 2+ years shipping LLM-powered products to real users. ~ Strong working knowledge of React, TypeScript, and Next.js enough to build, review, and guide production UI work. ~ Shipped streaming or conversational UI in production, including tool-calling patterns and structured model output. ~ Experience building or consuming MCP servers, or a comparable tool-integration layer for LLMs. ~ agent SDKs, LangGraph, or similar), including prompting, tool use, structured output, and multi-step agents. ~ Designed and run LLM evaluation frameworks, and used the results to make shipping decisions. ~ Practical understanding of LLM security and risk: prompt injection, data leakage, PII handling, and responsible AI practices. ~ Can reason about model cost, latency, and quality tradeoffs, and make model selection calls that hold up. ~ Strong back-end skills in Python and/or Node.js. You can design and build the services and APIs your AI systems need. ~ API experience across REST and/or GraphQL, plus working fluency with PostgreSQL and NoSQL stores. ~ Working knowledge of cloud platforms and their AI services (e.g., AWS Bedrock, Azure OpenAI, Google Vertex AI), CI/CD, and Docker. ~ Daily, hands-on use of AI coding agents in real work. Bonus: e-commerce, checkout, or payments experience in a high-volume environment. ~ Bonus: fine-tuning, classical ML, or data engineering experience for when a problem calls for it.
You ship production systems on time and at a high standard. You do well when requirements evolve and priorities shift. You've led technical teams through delivery and helped other engineers grow You explain technical concepts to non-engineers clearly. You can tell a client when AI isn't the right answer, and explain why. Senior-level competitive compensation with performance bonus upside. ~ High ownership and influence over technical direction of Thresh's AI practice. ~ Hybrid delivery flexibility with client travel and team co-locations. ~ Individual salaries that are offered to a candidate are determined after consideration of numerous factors including internal equity, market data, the candidate’s qualifications- including but not limited to specialty skills, prior relevant industry experience, relevant degrees or certifications.
Thresh Consulting — Strategy in Action for the AI Era Salary: $190,000 – $235,000 + up to 25% performance-based bonus Thresh Consulting is a modern digital consulting firm built for the AI era—where strategy, execution, and craft come together to deliver real outcomes with speed. Our specialty is helping organizations design, build, and scale digital products that create exceptional customer experiences and drive value.
We value clarity over complexity, quality over shortcuts, and learning as a core part of growth.
The Applied AI Engineering Lead turns ambition into production systems that move a client's business. You'll own the technical direction of our AI engagements: deciding which problems are worth solving with AI, architecting the solution, and leading the team that ships it. Your depth is in applied LLM engineering: agents, tool use, retrieval, evaluation, and the guardrails that make AI safe to put in front of customers. You're fluent enough across front end, APIs, data, and cloud to build an experience end-to-end yourself when it counts.
You'll spend about 70% of your time building and 30% leading. You'll guide a team of 2-4 engineers and serve as a primary partner to client engineering leaders. You'll also work with Thresh's practice leads to scope and shape new AI work.
Set technical direction for AI engagements
Partner with clients to identify and prioritize high-value AI use cases, and be clear about what's ready for production versus what's still a demo. Make build, buy, prompt, or fine-tune calls based on cost, latency, quality, and risk, and explain those calls to client leadership. Shape scope, estimates, and technical approach for new AI engagements alongside Thresh's practice and sales leads.
Build AI-powered, customer-facing experiences
Build conversational shopping experiences where customers discover, compare, and buy through natural language, from first question to completed order. Bring real business logic into the conversation: live pricing, inventory, promotions, personalization, and customer account context.
Build for a client's own site and app and for third-party AI assistants like ChatGPT and Gemini, all from one shared foundation. Build and extend MCP servers and tools that give models safe, structured access to catalog, pricing, cart, and order capabilities. Prepare commerce systems for AI agents that shop on a customer's behalf, with structured product data, agent-ready checkout, and clear rules for what an agent can and can't do. Integrate with enterprise commerce, catalog, order, and identity systems that were never designed for AI. Build retrieval and knowledge systems (RAG) over client content, catalogs, and documentation, and tune them for accuracy.
Make AI trustworthy in production
Prepare work for the legal, privacy, security, and brand reviews that every transactional AI experience goes through. Monitor model quality, drift, and spend after launch, and plan for model upgrades and vendor changes without breaking the experience.
Make AI coding agents (Claude Code, Cursor) your default way of working. Prototype conversational features in days with mock data, so clients can see the experience before the real integrations land. Mentor engineers through code review, pairing, and technical guidance, across the full applied AI stack, from prompting and evals to production architecture. Partner with product managers and designers on conversation design, scope, technical risk, and estimates. Define the components, patterns, and standards that become reusable assets across Thresh's AI work, including reference architectures and starter kits. Help build Thresh's applied AI capability through hiring input, technical interviews, and internal enablement. Represent Thresh's AI point of view with clients and platform partners through workshops, technical content, and pre-sales conversations.
Own AI solutions from discovery through production deployment. Explain progress and tradeoffs clearly to technical and non-technical stakeholders, including what AI can and can't reliably do. Hold the quality bar when deadlines tighten and requirements shift.
7+ years building and shipping production software, including 2+ years shipping LLM-powered products to real users. ~ Strong working knowledge of React, TypeScript, and Next.js enough to build, review, and guide production UI work. ~ Shipped streaming or conversational UI in production, including tool-calling patterns and structured model output. ~ Experience building or consuming MCP servers, or a comparable tool-integration layer for LLMs. ~ agent SDKs, LangGraph, or similar), including prompting, tool use, structured output, and multi-step agents. ~ Designed and run LLM evaluation frameworks, and used the results to make shipping decisions. ~ Practical understanding of LLM security and risk: prompt injection, data leakage, PII handling, and responsible AI practices. ~ Can reason about model cost, latency, and quality tradeoffs, and make model selection calls that hold up. ~ Strong back-end skills in Python and/or Node.js. You can design and build the services and APIs your AI systems need. ~ API experience across REST and/or GraphQL, plus working fluency with PostgreSQL and NoSQL stores. ~ Working knowledge of cloud platforms and their AI services (e.g., AWS Bedrock, Azure OpenAI, Google Vertex AI), CI/CD, and Docker. ~ Daily, hands-on use of AI coding agents in real work. Bonus: e-commerce, checkout, or payments experience in a high-volume environment. ~ Bonus: fine-tuning, classical ML, or data engineering experience for when a problem calls for it.
You ship production systems on time and at a high standard. You do well when requirements evolve and priorities shift. You've led technical teams through delivery and helped other engineers grow You explain technical concepts to non-engineers clearly. You can tell a client when AI isn't the right answer, and explain why. Senior-level competitive compensation with performance bonus upside. ~ High ownership and influence over technical direction of Thresh's AI practice. ~ Hybrid delivery flexibility with client travel and team co-locations. ~ Individual salaries that are offered to a candidate are determined after consideration of numerous factors including internal equity, market data, the candidate’s qualifications- including but not limited to specialty skills, prior relevant industry experience, relevant degrees or certifications.