• Design, build, and maintain scalable APIs, services, and data pipelines primarily in Python, leveraging microservices and cloud-native architectures. • Implement secure, high-performance, and maintainable system designs that can evolve with business and healthcare needs. • Develop web applications and dashboards (React/Next.js or similar) that surface backend and AI functionality to internal and external users. • Integrate and refine LLM workflows including prompt engineering, model orchestration, and backend/UX pipelines for intelligent healthcare solutions. • Ensure engineering excellence through code reviews, testing, CI/CD automation, observability, and performance optimization. • Guide technology decisions, frameworks, and best practices for software development. • Lead and mentor junior engineers and offshore development teams. • Stay up to date with industry trends and emerging technologies to drive continuous improvement. • Provide technical documentation and architectural diagrams for system enhancements. • Work closely with Product Management to define technical requirements and roadmap initiatives. • Collaborate with UX/UI teams to ensure seamless and intuitive user experiences. • Integrate backend APIs and microservices for efficient data exchange across platforms. • Partner with data analytics teams to optimize performance and scalability.

• Lead the design, implementation, and expansion of the company's data platform, including ingestion pipelines, data warehouse/lakehouse architecture, transformation layers, and data serving infrastructure. • Define and champion engineering standards, best practices, and architectural patterns for data infrastructure across the organization. • Collaborate cross-functionally to understand data needs and translate them into scalable, reliable platform capabilities. • Drive technical decisions on tooling, frameworks, and vendor selection for the data stack (e.g., orchestration, storage, transformation, observability). • Mentor and level up engineers on the team, providing technical guidance and code review with a focus on quality and long-term maintainability. • Proactively identify scalability, reliability, and performance bottlenecks and lead efforts to address them. • Partner with stakeholders across engineering, data science, and business teams to ensure the platform supports current and future analytical and operational needs. • Contribute to organizational planning and hiring, helping grow the data engineering function.

• Execute coding tasks within **agentic coding environments**, maintaining strict evaluation protocols • Review and evaluate **model-generated code trajectories** for correctness and completeness • Validate outputs by reading code, running tests, analyzing logs, and inspecting artifacts • Perform targeted validation using **scripts, tests, and manual checks** • Write **clear, evidence-based rationales** for evaluations and rankings • Design realistic, multi-step **coding tasks and workflows** (offline work) • Create and refine **evaluation rubrics and scoring criteria** • Ensure consistency, quality, and compliance across evaluations • Identify issues in environments, instructions, or workflows and report with clear evidence

• Build AI-native products that reshape how HR teams source, hire, and operate. • Design, build, and orchestrate AI agents and agentic workflows to accelerate product delivery. • Ship features end-to-end, from customer insight to production-ready experience, using AI-augmented development. • Prototype rapidly with coding co-pilots and agentic tooling, validate with customers, and iterate based on real-world feedback. • Architect and evolve systems to support performance, reliability, and scale, designing for agent-assisted operation from the start. • Turn ambiguous opportunities into clear execution plans and deliver measurable outcomes. • Multiply team output by sharing agentic workflows, elevating engineering practices, and raising the bar for speed and quality across the team.