Applied AI Engineer, Digital Natives
Build and deploy AI-powered products for digital-native customers, taking systems from experimentation through production and scale. The role requires strong Python skills, hands-on production engineering, systematic AI evaluation, and the ability to navigate reliability, security, governance, and customer impact.
About the job
Responsibilities
- Partner with digital-native customers to identify high-value opportunities and translate them into technical architectures, implementation plans, evaluation strategies, and measurable success criteria.
- Design, build, and deploy AI systems that solve customer problems and deliver measurable business outcomes.
- Write code for prototypes, evaluation harnesses, reference implementations, integrations, and production accelerators.
- Make technical decisions across models, agents, retrieval, tools, data, reliability, observability, latency, cost, safety, security, and governance.
- Diagnose implementation challenges, reproduce failures, test hypotheses, and resolve blockers.
- Help customers move prototypes into reliable production systems, sustained adoption, and scaled impact.
- Collaborate with customer engineering teams and internal Product, Research, Engineering, Security, and go-to-market teams.
- Create reusable architectures, tooling, playbooks, and technical guidance for enterprise deployments.
Requirements
- Demonstrated experience designing, building, and delivering AI or machine-learning systems in enterprise environments, including taking systems from prototype to production.
- Significant hands-on contributions in code, architecture, evaluation, debugging, or production engineering.
- High proficiency in Python and comfort working across an AI application stack.
- Ability to evaluate AI systems using representative data, graders, production signals, and human judgment.
- Experience with enterprise production requirements, including integrations, reliability, observability, security, privacy, data governance, performance, and cost.
- Ability to connect technical decisions to customer workflows, adoption, and measurable business outcomes.
- Clear communication with engineers, technical leaders, security teams, product leaders, and executives.
- Strong technical judgment, agency, collaboration, and end-to-end ownership in ambiguous environments.
Nice-to-haves
- Experience with JavaScript, TypeScript, or another relevant programming language.
- Background in applied AI or machine-learning engineering, forward-deployed engineering, software engineering, customer engineering, solutions architecture, or technical consulting.
- Experience with AI systems, enterprise deployments, or OpenAI products.
Skills
Python, JavaScript, TypeScript, Machine Learning, Ai Systems, Evaluation Systems, Model Deployment, Retrieval, Observability, Data Governance, Cloud Security, Production Engineering
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