Translate high-level business problems into functional AI solutions by creating and executing end-to-end solutions.
Design, build, and maintain backend services and APIs that deploy and integrate AI-powered features using existing foundational models (e.g., OpenAI, Claude, Hugging Face, etc.).
Implement scalable, reliable, and maintainable backend architecture supporting AI workflows, automation, and data processing pipelines.
Integrate AI services into complex technology stacks, ensuring interoperability with microservices, AWS platform, and data systems (Snowflake).
Enable rapid test-and-learn cycles by building experimentation frameworks that support iterative deployment of AI-driven functionalities.
Collaborate closely with cross-functional teams including product managers, data scientists, and frontend engineers to translate business needs into technical implementations.
Stay abreast of the AI ecosystem, frameworks, and tools to guide implementation decisions and technical evaluations for adopting new AI capabilities.
Document backend system designs, APIs, workflows, and integration patterns clearly for team knowledge sharing and operational continuity.

Bachelor’s or Master’s degree in Computer Science, Engineering, or related technical discipline.
Demonstrated experience as a backend software engineer with strong programming skills (Python preferred; experience with Ruby or Go is a plus).
Proven ability to design and develop scalable backend systems, APIs, and distributed services.
Strong understanding of cloud infrastructure and platforms, container orchestration, and deployment pipelines.
Strong knowledge of AI ecosystem components including major foundational model providers, AI APIs, data pipelines, and orchestration frameworks.
Practical expertise in integrating third-party AI services and managing complex dependencies in multi-stack environments.
Effective problem solver with a test-and-learn mindset, capable of rapid prototyping and iterative development.
Excellent communication skills to present technical concepts clearly to both technical and business stakeholders.
Familiarity with AI tools and frameworks like AWS Bedrock, Langchain, vector databases, or similar AI orchestration technologies.
Experience with automation, knowledge systems, and backend optimization related to AI-driven processes.
Understanding of responsible AI principles, data privacy, and system reliability considerations.
Experience in building APIs and backend components for real-time or near-real-time AI applications.

Applied AI Engineer

Key skills