Senior AI Engineer

evelop end-to-end AI features, from data ingestion and feature engineering to inference and feedback loops.

• Partner with Product and UX teams to shape AI-driven user experiences, ensuring usability, explainability, and trust.

Prompt Engineering & Optimization

• Design, develop, and optimize prompt strategies for large language models to ensure accuracy, consistency, and relevance in production use cases.

• Implement prompt versioning, experimentation, and A/B testing to support rapid iteration and data-driven improvements.

• Continuously evaluate and refine prompts using offline evaluation, automated testing, and live telemetry to reduce hallucinations and improve output quality.

• Optimize prompt and model usage to balance response quality, latency, and cost efficiency at scale.

• Collaborate with UX and Product to align prompt behavior with user intent, tone, and experience expectations.

Engineering & Platform Excellence

• Develop scalable backend services using Java and/or Python, deployed in AWS cloud environments.

• Design and optimize API-driven architectures that integrate AI services securely and reliably across the platform.

• Work with SQL and NoSQL databases to support transactional and analytical workloads.

• Apply strong engineering fundamentals around performance, reliability, observability, and security.

• Contribute to architectural decisions that ensure long-term scalability and maintainability.

Leadership & Collaboration

• Provide technical leadership in AI and system design discussions.

• Mentor engineers and help raise the overall AI and engineering maturity of the team.

• Collaborate cross-functionally with Data Science, Platform, UX, DevOps, and Security teams.

• Influence technical roadmaps and align AI capabilities with Workforce AI product goals.

You have what it takes if you have

• 8-10 years of professional software engineering experience.

• Strong hands-on experience with Java and/or Python.

• Proven experience delivering AI/ML or Generative AI systems in production.

• Hands-on expertise in prompt engineering and prompt optimization for LLM-powered applications.

• Solid experience with AWS and cloud-native system design.

• Strong working knowledge of SQL and NoSQL databases.

• Ability to design systems that balance AI quality, scalability, latency, and cost.

Extra dose of awesome if you have...

• Experience with LLMs, RAG architectures, vector databases, and structured output enforcement.
• Familiarity with MLOps practices (model versioning, monitoring, evaluation).
• Experience designing AI-powered user experiences in partnership with UX teams.
• Background in enterprise SaaS or workforce/HR technology.
• Experience taking AI products from concept to production.