• Engage with the Digital and Innovation (D&I) leaders to promote the D&I vision • Assume responsibility for the success of the enterprise data platform, data engineering delivery, and AI architecture ensuring Westinghouse digital solutions • Lead the data engineering and AI architecture team in establishing the data architecture and data fabric • Establish integration patterns and platform services to promote the use of industry data across multiple use cases • Mentor team members responsible for cloud architecture and infrastructure; establish best practices for cloud platforms, security, availability, cost management, and operational support according to Westinghouse Information Technology • Develop team member critical skills such as Data architecture, data modeling, and governance Data ingestion, transformation, and quality (ETL/ELT) Data integration, master data, and data interoperability

Role Description As a Mid-Level AI Engineer, you sit at the intersection of AI-assisted development and software quality assurance. You won't be writing boilerplate — you will co-create with LLMs, mature our in-house QA testing framework, and ensure our AI workflows are reliable, predictable, and built to scale. This role is ideal for a builder who thrives in early-stage environments, loves experimentation, and takes real pride in clean, well-tested code. - Embrace the "vibe coding" philosophy — use LLMs and AI coding assistants to rapidly prototype, write, and iterate on Python applications. - Design, execute, and maintain automated test suites using Alambda's proprietary in-house QA tools for complex software systems. - Work closely with foundational models to optimize prompts, evaluate outputs, and handle edge cases in LLM-driven features. - Maintain and improve internal Python scripts and testing frameworks to ensure seamless integration with our core product. - Partner with product and engineering teams across global time zones (US EST/PST) to ensure software alignment and rapid deployment. Qualifications - 3+ years of professional Python experience — writing clean, readable, and maintainable code. - Hands-on experience integrating LLM APIs (OpenAI, Anthropic Claude, Llama, or similar), including prompt engineering and API output management. - Strong QA fundamentals: unit testing (pytest / unittest), integration testing, and regression testing at scale. - Solid Git workflow and familiarity with CI/CD pipelines (GitHub Actions, GitLab CI, or equivalent). - Active use of AI coding companions (Copilot, Cursor, Windsurf) and the ability to audit AI-generated code for security and performance issues. - High tolerance for ambiguity, a passion for rapid iteration, and the ability to pivot quickly based on AI outputs and prototyping results. - Excellent English communication skills — written and verbal — and proven ability to work autonomously in a fully remote, async-first environment. Requirements - Experience building or meaningfully contributing to internal Python frameworks, SDKs, or developer tooling beyond standard application code. - An active open-source or personal AI/ML project portfolio — GitHub repos, experiments, technical blog posts, or community contributions — that demonstrates self-driven exploration of the AI landscape. Equal Opportunity Employer AspenView is proud to be an equal opportunity employer. We believe in creating an environment where all employees feel welcome, valued, and empowered to succeed. We celebrate diversity and strive to build a culture of inclusion where all individuals, regardless of their race, color, gender, gender identity or expression, sexual orientation, disability, age, or any other characteristic, can thrive. We encourage applicants from all walks of life to join our team and make a lasting impact.

• Own the design and delivery of high-performance, low-latency APIs and microservices powering vehicle history (VHR), VIN intelligence, and transaction systems. • Build and scale Java/Spring Boot services in a cloud-native AWS environment. • Architect and implement event-driven systems using Kafka/SQS to support asynchronous processing and data pipelines. • Lead development of complex features, platform enhancements, and proof-of-concepts that drive business impact. • Implement and improve CI/CD pipelines, automated testing, and observability.

• Ship zero-to-one AI products end-to-end — from customer discovery and prototyping through production deployment and iteration. • Build agentic AI systems — design and implement autonomous and semi-autonomous workflows using LLMs, tool-use, memory, and orchestration. • Develop AI tools that improve efficiency across clinical operations, data extraction, manual workflows, and more. • Commercialize AI research — partner with the AI lab to take proprietary models from research prototypes and into production. • Contribute to model training and post-training — fine-tuning, evaluation, safety testing, and optimization of Natera's proprietary oncology models.