As an employer, Abbott is interested in candidates who are passionate about creating healthy solutions and making a difference in the world. Abbott offers compe
Senior AI Engineer
Location
United States
Posted
4 days ago
Salary
$99.3K - $198.7K / year
Seniority
Senior
No structured requirement data.
Job Description
Senior AI Engineer
Abbott
Role Description We’re focused on helping people with diabetes manage their health with life-changing products that provide accurate data to drive better-informed decisions. We’re revolutionizing the way people monitor their glucose levels with our new sensing technology. This Senior AI Engineer position can work remotely within the U.S. Senior AI SW Engineer to work on a Go-based medical device web application and a modern Software as a Service (SaaS) cloud platform aimed at enhancing user health. This role will help improve existing platforms, design and implement cloud-based services, build internal tooling, and bring practical AI capabilities into safe, reliable health-focused products. What You’ll Do - Enhance and maintain our Go-based healthcare platforms, focusing on reliability, performance, security, scalability, and user experience. - Design, build, test, and deploy cloud-native SaaS services, APIs, and data workflows using Go and modern engineering practices. - Develop high-volume, low-latency distributed systems supporting a global healthcare platform. - Design and integrate AI capabilities, including model integrations, inference services, and evaluation workflows to drive advanced features and health insights. - Collaborate with internal and external partners to build secure APIs that enable compliant sharing and use of medical data. - Partner cross-functionally with product, quality, regulatory, security, DevOps, and engineering teams to deliver safe, scalable, and compliant medical device software. - Work with InfoSec to design and implement secure, standards-aligned solutions. - Write clean, testable, and maintainable code with strong test coverage. - Ensure performance, availability, and scalability through high-quality engineering and design practices. - Contribute to architecture and technical strategy, translating architectural goals into clear service boundaries, reusable patterns, and maintainable implementations. - Drive system improvements to align platforms with evolving architectural standards. - Build internal tools, automation, dashboards, and developer utilities to improve engineering productivity, operational visibility, and release confidence. - Document system design using diagrams, flowcharts, and technical specifications. - Participate actively in agile development, contributing to planning, development, testing, and delivery. - Lead technical discussions, design reviews, and code reviews to uphold engineering excellence. - Stay current with AI, cloud, healthcare, and software engineering trends, applying relevant innovations. - Demonstrate ownership, strong technical judgment, collaboration, and a commitment to continuous learning. Qualifications - Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field. - 8+ years of relevant experience, or a Master’s degree with 6+ years of experience. - Hands-on experience with applied AI/ML concepts, including model integration, inference workflows, evaluation, feature engineering, prompt/model orchestration, or intelligent automation. - Strong backend engineering experience in Go, including APIs, concurrency, testing, and maintainable system design. - Experience designing and contributing to scalable, highly available, and observable system architectures. - Experience building and maintaining distributed systems, including asynchronous processing, messaging, and backend data workflows. - Strong experience developing and integrating RESTful APIs and microservices. - Familiarity with database technologies (e.g., SQL Server, PostgreSQL, or similar relational/NoSQL systems). - Experience with cloud platforms and modern development practices, including CI/CD, containers, infrastructure automation, observability, and version control. - Solid computer science fundamentals and problem-solving skills. - Experience working in agile development environments. - Practical working knowledge of Linux systems. - Strong communication skills (written and verbal) and ability to collaborate across teams. - Demonstrated ownership, curiosity, and willingness to contribute beyond defined responsibilities. Preferred Qualifications - Experience with medical device software, digital health, wellness platforms, clinical applications, or regulated software development. - Familiarity with quality system practices, validation support, requirements traceability, risk-aware development, audit trails, and design documentation. - Experience with Kubernetes, Docker, Terraform, message queues, event-driven architectures, service observability, or platform engineering practices. - Exposure to MLOps, model versioning, monitoring, evaluation frameworks, retrieval-augmented generation, vector databases, or responsible AI practices. - Understanding of secure software development, privacy, access control, auditability, encryption, and responsible handling of health-related or sensitive user data. - Ability to mentor teammates through code reviews, documentation, knowledge sharing, and constructive technical feedback. Benefits - Career development with an international company where you can grow the career you dream of. - Employees can qualify for free medical coverage in our Health Investment Plan (HIP) PPO medical plan in the next calendar year. - An excellent retirement savings plan with high employer contribution. - Tuition reimbursement, the Freedom 2 Save student debt program, and FreeU education benefit - an affordable and convenient path to getting a bachelor’s degree. - A company recognized as a great place to work in dozens of countries around the world and named one of the most admired companies in the world by Fortune. - A company that is recognized as one of the best big companies to work for as well as a best place to work for diversity, working mothers, female executives, and scientists.
Related Guides
Related Job Pages
More AI Engineer Jobs
• Building and deploying AI agents and agentic workflows using the client's internal agent framework and modern orchestration tools (LangChain, LangGraph, CrewAI, AutoGen, or similar) • Designing and implementing RAG pipelines - including chunking strategies, embeddings, and vector store integrations (Pinecone, Weaviate, pgvector) • Processing and structuring financial documents (PDFs, DOCX reports, tables, CIM/DDQ materials) into clean, machine-readable outputs via Python • Integrating REST APIs and cloud services to connect agent workflows with existing business systems and data infrastructure • Owning the full delivery cycle for each initiative: scoping, development, testing, deployment, and handover to business users • Instrumenting agents for observability, writing test harnesses for non-deterministic behaviour, and ensuring failures are explicit and handled gracefully • Communicating proactively with the client-side team - surfacing blockers early, documenting solutions clearly, and keeping stakeholders in the loop without being asked
• Serve as the technical liaison between Nsight and the IVA provider • Manage day-to-day performance against quality and HIPAA compliance standards • Define acceptance criteria and build layered test harnesses for all provider releases • Configure and tune AI phone agents to meet clinical and operational goals • Build and own the alerting layer • Architect AI-driven quality intelligence pipelines • Build PHI-safe audio processing pipelines • Build the tooling the work requires
• Partner with business and technical stakeholders to understand workflows, challenges, and success metrics • Translate ambiguous problems into clear technical solutions, designs, and delivery plans • Design and build AI-powered applications using LLMs, APIs, and enterprise data systems • Develop production-grade backend services using Python and frameworks like FastAPI or Flask • Build and maintain RAG systems, including document ingestion and normalization, chunking, metadata, and embedding strategies, vector, keyword, and hybrid search, reranking and relevance tuning, source attribution and grounding • Integrate AI solutions with SharePoint, Microsoft Graph, SQL databases, internal APIs, and business applications • Design secure systems that respect access control, governance, and enterprise compliance requirements • Build observable, reliable, and maintainable AI workflows in production environments • Establish evaluation frameworks for LLM systems (accuracy, groundedness, latency, completeness, and failure modes) • Iterate rapidly through prototypes, pilots, and production releases based on user feedback • Collaborate with engineering, data, security, and product teams to ensure adoption and long-term sustainability • Make pragmatic technology choices across Azure OpenAI, Azure AI Search, and open-source tools • Document architecture, decisions, trade-offs, and operational requirements.
• Design, build, and optimize ML/DL models for production-scale audio deepfake detection, ensuring robustness across diverse real-world conditions including compression artifacts, noise, telephony, and streaming pipelines. • Partner with clients to develop a deep understanding of their production environments and define model performance criteria. • Investigate failure cases in client environments, build custom evaluation frameworks, and implement mitigation strategies spanning both Engineering and AI. • Design and execute structured experimentation roadmaps aligned with client requirements and proactive system resilience goals. Translate findings into clear and actionable insights. • Monitor, measure, and report on model performance in production using data analytics and AI observability tools (e.g. Datadog, Metabase). Identify degradation trends, data drift, and emerging threat patterns before they impact client outcomes. • Build and maintain dashboards and analytics pipelines that surface model health metrics, enabling data-driven decisions across AI, Engineering, and Product teams. • Collaborate with cross-functional partners — Applied Scientists, Deployment Engineers, and Product teams — to deploy scalable, production-grade models with clear performance benchmarks and monitoring in place.




