Applied AI Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for an Applied AI Engineer based in United States.

This role focuses on building production-ready AI applications that people use to solve real business challenges.

You will take validated AI concepts from prototype through deployment, operationalization, and successful handoff to engineering teams.

Working in fast-paced 2-to-4-week sprint cycles, you will combine full-stack engineering, AI development, product thinking, and user experience.

Your work will help reduce manual effort, accelerate product initiatives, and enable AI-powered workflows across the organization.

You will build on modern agent infrastructure while ensuring AI applications are observable, measurable, secure, and reliable in production.

The role offers significant ownership, requiring you to move from ambiguous briefs to shipped solutions while collaborating across engineering, data, infrastructure, and business teams.

It is an opportunity to help demonstrate what AI-native software delivery can achieve within a global, virtual-first environment.

Accountabilities:

• Lead the architecture and implementation of AI-powered full-stack applications across active delivery initiatives, taking solutions from initial wireframes through polished, production-quality interfaces.
• Build and integrate AI applications using agent infrastructure, agent skills, vector databases, workflow patterns, and intelligence-layer capabilities while maintaining a high standard of frontend and UX quality.
• Translate validated proof-of-concept blueprints into production applications with real users, measurable outcomes, evaluation frameworks, and observability instrumentation.
• Develop evaluation harnesses, instrumented traces, cost budgets, and monitoring capabilities that make AI behavior measurable, inspectable, and trustworthy in production.
• Integrate applications with core data infrastructure, including Snowflake, internal APIs, and API gateways, while following established connectivity and security standards.
• Embed application security, data governance, personally identifiable information handling, and relevant data requirements directly into application architecture and implementation.
• Participate in technical scoping by estimating delivery effort, identifying integration dependencies, assessing the applicability of AI agents, and defining measurable success criteria before development begins.
• Own applications through deployment and operational validation, confirming they are live and available to users before completing a structured handoff to the appropriate engineering or platform team.
• Create practical documentation for maintaining teams and contribute architectural decision records and technology evaluations for emerging application-layer technologies.
• Conduct lightweight user testing with business partners and use feedback to improve the usability, effectiveness, and adoption of delivered solutions.

Requirements:

• Demonstrated experience building and deploying production AI applications that real users depend on, with the ability to discuss production failures, troubleshooting, and lessons learned.
• Strong full-stack engineering capabilities, including frontend development with React preferred and backend experience with REST and/or GraphQL, authentication patterns, and cloud-native service architectures.
• Hands-on experience with agentic frameworks or LLM APIs such as LangChain, LlamaIndex, Anthropic, OpenAI, or equivalent technologies.
• Experience implementing RAG patterns, streaming responses, and intuitive user interfaces that make AI-powered functionality understandable and usable.
• Experience designing evaluation harnesses and LLM observability solutions using tools such as Langfuse, LangSmith, Braintrust, or equivalent is highly valued.
• Familiarity with multi-model routing and inference cost optimization using technologies such as LiteLLM, Portkey, or equivalent is a plus.
• Experience with data platforms such as Snowflake or BigQuery and practical knowledge of AI application security, including PII handling, secrets management, prompt-injection defense, and API audit logging.
• Strong product intuition and the ability to prioritize genuine user needs when selecting models, architectures, and technical approaches.
• Ability to write design documentation, conduct postmortems, and clearly explain technical or model-related failures to non-technical stakeholders.
• A proactive, ownership-oriented mindset with the ability to work from an ambiguous brief, move quickly toward implementation, and remain accountable for outcomes beyond the code itself.
• Familiarity with the organization’s internal platforms, data infrastructure, or product ecosystem is valued as it can accelerate onboarding and early impact.
• Candidates must be legally authorized to work in the country where employment is offered; employment sponsorship is not provided.

Benefits:

• Starting salary of $130,000-$160,000 USD or $110,000-$130,000 CAD per year, depending on skills, education, qualifications, experience, performance, business needs, and geographic location.
• Full-time, virtual-first work environment with remote opportunities across the United States, Canada, Europe, or Australia.
• Flexible work arrangements designed to support effective remote collaboration and work-life balance.
• Learning and professional development opportunities within a global, technology-driven environment.
• Opportunity to work on production AI applications with measurable business impact and exposure to modern AI engineering practices.
• Collaborative environment spanning engineering, product, data, infrastructure, analytics, and business teams.
• Commitment to an inclusive workplace and reasonable accommodations throughout the recruitment process.