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United States
Remote only
Not Available
This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Forward Deployed AI Engineer (GenAI, AWS) based in the United States.
This role is designed for a senior, hands-on engineer who wants to combine advanced AI engineering with direct ownership of business outcomes. You will work alongside enterprise customers to understand their operations from the inside, redesign critical workflows, and build production-ready generative AI systems that deliver measurable impact. Rather than relying on traditional requirements gathering, you will immerse yourself in real business functions before determining what to automate and how. You will develop agentic applications, LLM-powered workflows, and evaluation frameworks across financial services, insurance, healthcare, and related industries. Working in small, senior teams, you will collaborate with business leaders, operators, and technical stakeholders to drive adoption and continuous improvement. This remote-friendly opportunity offers the autonomy to shape solutions from first principles while remaining deeply involved in the code.
You will take ownership of the full journey from understanding a customer's operational challenges to building, deploying, and improving AI-powered solutions. Your responsibilities will include:
Understand customer operations firsthand: Spend the initial two to four weeks of an engagement performing the operator's role, learning the workflows, constraints, and practical challenges that shape the business function.
Learn new business domains quickly: Develop working expertise in areas such as insurance underwriting, healthcare revenue cycle management, claims processing, and asset management, engaging directly with the people responsible for these functions.
Redesign business processes: Collaborate with operators and Forward Deployed Executives to rethink workflows from first principles, combining discovery, user research, technical design, and implementation planning.
Build production-grade GenAI systems: Develop and deploy LLM applications, agentic workflows, retrieval systems, structured-extraction pipelines, backend services, and supporting data infrastructure.
Establish rigorous evaluation frameworks: Build evaluation harnesses before implementing features, define measurable success criteria, instrument system performance, and use evaluation results to guide design decisions.
Deliver end-to-end engineering solutions: Write production code across backend services, data pipelines, and AI components, primarily using Python and TypeScript while adapting technologies to customer requirements.
Deploy and operate cloud-native systems: Deliver secure, observable, containerized, and maintainable solutions on AWS, or on GCP and Azure when required by the customer.
Improve and maintain industry blueprints: Start from existing, working AI solutions and incorporate lessons learned from customer engagements to strengthen reusable systems and accelerate future deployments.
Own measurable business outcomes: Partner with Forward Deployed Executives responsible for business unit KPIs and align engineering decisions with the same performance objectives.
Drive adoption and operational change: Ensure solutions become part of everyday workflows, working with users and stakeholders to address adoption barriers and improve business processes.
Collaborate with stakeholders at every level: Build credibility with customer engineers, operational teams, and senior executives, constructively challenge assumptions, and communicate trade-offs clearly.
Influence delivery commitments: Help define project scope, technical feasibility, and implementation priorities before commitments are made, taking responsibility for what will ultimately be built and delivered.
The ideal candidate is an experienced software engineer with strong applied AI expertise, sound technical judgment, and the confidence to work autonomously in complex customer environments. The following qualifications are expected:
At least 8 years of software engineering experience, including substantial hands-on responsibility for production code. You should be actively coding and committed to remaining an individual contributor.
A genuine willingness to spend several weeks performing an operational role before designing its automation. This position requires direct immersion in customer workflows rather than working exclusively from an IDE or technical specifications.
Proven ability to learn unfamiliar business domains quickly and develop enough expertise to challenge assumptions and collaborate effectively with domain specialists.
Demonstrated experience shipping generative AI and LLM systems into production, including managing the technical and operational challenges that emerge beyond the prototype stage.
Experience building or owning evaluation suites for nondeterministic systems, with the ability to explain evaluation methods, metrics, and their relevance to business outcomes.
Strong software engineering fundamentals and the ability to become productive in unfamiliar codebases or programming languages.
Proficiency in Python and/or TypeScript, with engineering depth and practical experience valued over familiarity with a particular technology stack.
Experience delivering cloud-native applications on AWS, including containers, Kubernetes or Amazon ECS, infrastructure as code (IaC), CI/CD pipelines, and production operations. GCP or Azure experience is an advantage.
Strong foundations in AI and machine learning, including an understanding of model capabilities, limitations, and failure modes.
Hands-on production experience with Claude Code and Cowork.
Ability to communicate credibly with senior business leaders, CTOs, engineers, and operational teams, adapting discussions to technical and business audiences.
Comfort with ambiguity, independent decision-making, and end-to-end ownership in engagements where requirements are intentionally not fully defined at the outset.
Fluency in written and spoken English.
Additional qualifications that would be an advantage:
Previous experience as a founder, CTO, or engineering leader who has chosen to return to hands-on individual contribution.
Deep expertise in financial services, insurance, healthcare, or asset management.
Experience in consulting, professional services, or embedded customer-facing engineering roles.
Knowledge of data platforms, including data lakes, warehouses, streaming and real-time analytics, data mesh, data contracts, governance, and data quality.
Experience with MLOps and traditional machine learning tools such as PyTorch, SageMaker, or MLflow.
Expertise in model fine-tuning, distillation, inference optimization, or model serving.
Experience with graph databases such as Neo4j or Amazon Neptune.
Advanced infrastructure-as-code skills using AWS CDK, CloudFormation, or Terraform.
Open-source contributions or public writing focused on applied AI.
Base salary of up to $200,000 per year.
On-target earnings (OTE) of up to $250,000, including applicable variable compensation.
Unlimited paid time off (PTO) policy.
401(k) profit-sharing plan available after a three-month probationary period.
Opportunity to work on advanced AI delivery initiatives involving Cowork Activation, Agentic SDLC, and industry-specific AI blueprint activations in financial services and healthcare.
The opportunity to influence how leading enterprises adopt AI, from strategic planning through initial production deployment.
Collaboration within small, senior delivery teams alongside Principal Architects and Forward Deployed Engineers.
The opportunity to help develop AI delivery tools, frameworks, and reusable engineering practices.
Remote-friendly working culture.
