Roles worth a look

Building internal agentic tooling to run support and GTM at a small LLM-infrastructure company — precisely his orchestration, automation and internal-tools strength.

Forward-deployed engineering building custom solutions on OpenAI APIs for customers is his ideal shape.

Forward-deployed applied-AI engineering building production LLM systems with customers — exactly his operator-tier archetype at an AI employer.

Forward-deployed AI engineering on an LLM/agent observability platform is exactly the operator-tier applied-AI archetype he targets.

Same strong forward-deployed AI engineering fit at an agent-evaluation platform, customer-facing and LLM-centric.

Forward-deployed engineering owning end-to-end LLM deployments with customers — the exact operator-tier applied-AI archetype.

Forward-deployed engineering built around orchestrating AI agents with customers to ship outcomes — exactly his operator-tier applied-AI shape, with pay above his floor.

Forward-deployed engineering focused on AI and agentic workflows — customer-facing applied-AI delivery, exactly his target shape.

Forward-deployed AI solution architecture with LLMs, RAG and workflow orchestration plus customer discovery — exactly the operator-tier applied-AI shape he wants.

Applied AI Engineer deploying LLM/agentic use cases with customers on top of existing models is a squarely on-target fit.

AI Solutions Engineer building internal agents and workflow automation — a direct match for his orchestration and automation shipping.

Unlevelled full-stack role building LLM-powered product features with prompt engineering and model evaluation — exactly his applied-AI shape.

Hands-on Forward Deployed Engineer building AI-powered applications with enterprise customers — ideal operator-tier applied-AI fit.

Forward Deployed Engineer taking AI-assisted agent workflows into production with customers — his exact operator-tier archetype.

AI provider operations, model launches, benchmark tooling and internal automation is precisely operator-tier applied-AI work.

Forward-deployed engineer architecting and deploying production AI applications with customers — ideal operator-tier fit.

Forward-deployed engineer owning end-to-end AI solution delivery with Python and API integration — exactly his operator-tier applied-AI archetype, with strong disclosed pay.

Forward-deployed engineer prototyping AI workflow solutions on an existing AI platform — precisely his orchestrator/operator shape.

AI Automation Engineer mapping business processes with SMEs and delivering AI-first automations is exactly his operator-tier applied-AI shape, IC scope and remote.

Founding AI engineer shipping agentic workflows, MCP, RAG and internal AI tooling in Python/TypeScript — squarely his orchestration-and-ship profile.

Embedded forward-deployed PM deploying agents and driving automation for customers is precisely his operator-tier AI archetype.

Applied AI specialist driving AI adoption with customers at an AI-powered operations company — operator-tier and customer-facing, ideal shape.

Product engineer building internal supply-chain and finance apps with agentic workflows and integrations at an AI lab — his domain plus his stack.

Forward deployment engineering at a genAI platform company — deploying and adapting models for customers is precisely his archetype.

AI adoption and deployment lead owning enterprise activation of generative-AI agents — the operator-tier AI enablement shape he is actively seeking.

Building agentic prototypes and demo tooling for customer-facing teams at OpenAI is exactly his orchestrate-and-ship profile.

Forward-deployed AI engineer building production GenAI applications with customers — exactly the operator-tier applied-AI archetype.

Forward-deployed agentic workflow engineering for clients is exactly his operator-tier applied-AI shape.

Enterprise AI adoption and deployment lead at a generative-AI platform — exactly the AI enablement/forward-deployed archetype he wants.

Forward deployed engineer at an AI developer cloud — customer-facing technical delivery on AI infrastructure, squarely his archetype.

Forward deployed engineer on generative AI within a spend/supply-chain platform hits both his applied-AI and domain strengths.

Same forward-deployed generative-AI role inside a spend-management platform, aligning applied AI with his procurement domain.

Prototype-to-production LLM deployments embedded with customers at OpenAI — the operator-tier applied-AI role he is targeting.

Forward Deployed Engineer prototyping and shipping LLM workflows with customers — the exact operator-tier applied-AI archetype.

Solutions architect deploying enterprise LLM/agent platforms for customers — exactly the operator-tier applied-AI archetype he fits.

Unlevelled AI engineer role building enterprise LLM/agent solutions on an existing platform — squarely his applied-AI wheelhouse.

Forward deployed engineer integrating, customizing and deploying AI solutions in customer environments — exactly his operator-tier applied-AI archetype, with pay above his band.

Customer-facing applied AI engineering delivering GenAI solutions at an AI company — squarely the forward-deployed/solutions archetype he wants.

Forward-deployed AI enablement building production agents embedded in a business function — exactly his operator-tier archetype (seniority uncapped for FDE).

Customer-facing solutions architecture for adopting and operationalizing LLM/agentic applications — squarely operator-tier applied-AI work.

Associate-level, AI-driven value engineering at an enterprise AI process-mining vendor — customer-facing prototypes and solutions where his process/supply-chain domain depth is the qualification.

AI-native business systems role building LLM automations, agents and MCP integrations at an AI company — operator-tier automation squarely in his zone.

Solution engineering for automation/AI in retail and supply chain — his domain expertise plus AI/automation demos and workshops, and seniority is fine on customer-facing titles.

Applied AI full-stack role at an AI-native company that explicitly values what you've built over credentials.

Forward deployed engineer at an AI infrastructure company solving customer technical problems — a core archetype for him.

Unlevelled Applied AI Engineer building agentic AI features — squarely the applied-AI IC role he wants.

Unlevelled Applied AI Engineer role building agent-observability features end to end from prototype to production — squarely his shape.

Forward-deployed AI engineer owning enterprise delivery end to end — exactly the operator-tier customer-facing archetype he wants.

Forward-deployed customer engineering driving agentic-workflow adoption and deployments — exactly the operator-tier archetype, and seniority is fine on this title.

Forward-deployed product/engineering motion at a generative-AI healthcare company — exactly the operator-tier, customer-deployment shape he wants.

Forward-deployed AI architect work — deploying LLM/agentic solutions, driving adoption for customers — is squarely the operator-tier archetype.

Enterprise applied-AI architect owning deployment and adoption of ChatGPT/API/agents is exactly his operator-tier target.

Operator-tier AI enablement: owning technical enablement while designing and deploying agentic AI systems is exactly the AI-native practitioner shape he wants.

Designing, building and evaluating agentic AI workflows for business processes is exactly his operator-tier AI adoption/enablement shape.

Applied AI Engineer building AI-powered product features and automation on existing models — squarely his shipping profile, remote and unlevelled.

Embedded forward deployed engineer rapidly prototyping AI workflow solutions inside a business function — a top-fit operator-tier archetype.

Agentic Python engineer building self-improving loops and agent products, explicitly welcoming autodidacts and agentic-coding power users.

Forward deployed engineering on an LLM/agent platform is an excellent fit, though the APJ/Singapore base is a practical mismatch.

Building custom solutions on OpenAI APIs with customers is exactly his orchestration-and-delivery strength, though the SWE bar is a mild stretch.

Customer-embedded engineering on OpenAI APIs matches his applied-AI delivery profile with strong comp.

AI support engineering driving customer adoption and troubleshooting deployments at OpenAI — operator-tier AI enablement he can genuinely land.

Hands-on forward-deployed engineer building and deploying AI-enabled solutions with business stakeholders — his exact shape of work.

AI transformation lead driving enterprise AI adoption and deployment — operator-tier AI enablement work he is well suited to.

Forward-deployed engineering on an AI healthcare platform — customer-facing deployment of agents and AI workflows, an archetype he fits and seniority is not capped here.

Unlevelled software engineer on an AI Enablement team shipping RAG and LLM internal platforms — squarely applied-AI operator work.

SWE II on a zero-to-one agentic AI platform team is mid-level IC applied-agent work building on existing models — strong fit.

Solutions engineering on an enterprise LLM/agent platform — demos, POCs and customer deployments of AI, squarely his fit.

Full-stack AI engineer owning LLM/agent features end to end in a small profitable startup, with curiosity valued over credentials.

Technical account management guiding customers through agentic automation programs across supply chain and manufacturing — his domain plus AI adoption work.

Product engineer shipping agentic AI product experiences end to end at an AI-native company — ownership-driven, unlevelled IC scope that matches his build style.

Building agentic platform capabilities and shipping agents for manufacturing use cases — applied AI plus his manufacturing domain, IC scope.

CX operations engineering at an agentic AI company is exactly the operator-tier automation/internal-tooling shape he wants.

Forward-deployed AI deployment work at OpenAI is his archetype, though defense/intelligence stakeholders imply clearance-adjacent friction.

Unlevelled AI Engineer building agentic product features and internal agentic workflows at an AI infra company — squarely his shape.

Applied AI Engineer deploying agentic workflows into customer engineering teams — ideal operator-tier applied-AI fit, though based in Sydney.

Supply-chain/ops program management inside OpenAI's AI hardware org is the domain-depth-into-an-AI-company archetype, with hardware NPI ramp as the stretch.

Material planning and supply execution at an AI-infrastructure employer matches his MRP/planning depth directly.

Supply chain inside an AI-infrastructure org is exactly the domain-depth archetype, though the manager scope leading a multidisciplinary team is a stretch above IC.

Supply-chain planning systems product management inside an AI-driven operations company — his MRP/planning domain depth is the qualification.

Procurement enablement at OpenAI blends his procurement/ops domain with an explicit mandate to apply AI and automation to workflows — strong domain-depth-into-AI-company fit, though 'Lead' scope stretches him.

Sourcing/procurement engineering at an AI-powered defense company matches his supplier and BOM cost work, though senior scope stretches him.

Procurement function-building inside an AI infrastructure company — domain-depth into an AI employer, though it's a lead/foundational scope beyond pure IC.

Sourcing/NPI supply-chain work inside a frontier AI lab matches the domain-depth archetype, though it demands deep hardware/electrical NPI engineering experience he lacks.