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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Principal Software and AI Engineer - **Company:** Beone Medicines Ltd. - **Location:** United States (Remote available) - **Experience:** Expert - **Salary:** $137,000.0 - $182,000.0 - **Contract:** Permanent contract - **Skills:** .NET Framework, Artificial Intelligence, Amazon Web Services, Data Analysis, Microsoft Azure, Mobile Application Development, Cloud Computing Security, Code Review, Continuous Integration, Data Stores, Elasticsearch, Graph Database, Python (Programming Language), PostgreSQL, Machine Learning, Neo4j, Systems Development Life Cycle, System Testing, Enterprise Software Applications, Ontology Design, Multi-Agent Systems, Prompt Engineering, AI Coding Agents, Backend, Agentic-AI, Knowledge Engineering, Performance Monitor, Machine Learning Operations, Front End Software Development, Human in the Loop, GXP - **Published:** October 17, 2026 - **Apply:** https://www.builtincolorado.com/job/sr-principal-software-and-ai-engineer/11731399?handler=ApplyRedirect ## About the Role * Education: Bachelors with 7+ years, Masters with 5+ years or PhD with 3+ years * Experience: Software and AI/ML engineering experience, including experience as a tech lead or principal engineer owning large-scale AI products end to end. Experience in the pharmaceutical or life sciences industry is strongly preferred. * Track Record: Proven success shipping production-grade AI and agentic applications that deliver measurable scientific or business impact, ideally in a regulated environment. Core Technical Expertise * Full-Stack Software Engineering: A strong software engineer first. Deep backend expertise in Python (preferred) and/or .NET, with a track record of architecting and building enterprise applications end to end across frontend, APIs, databases, and deployment. Hands-on experience with relational, search, and graph data stores such as PostgreSQL, Elasticsearch, and Neo4j. Sound judgment in system design, data modeling, and production operations. * Multi-Agent, AI-Native Applications: Deep, hands-on experience architecting and shipping multi-agent applications to production, with strong command of agent harness design, context engineering, orchestration, tool integration (including MCP), and human-in-the-loop controls. * Frontier Model Application: Strong judgment in selecting and applying frontier and open-weight models, using structured outputs, guardrails, and validation to ensure stable, robust model behavior. Experience with fine-tuning or post-training is a plus. * Knowledge Engineering: Proven ability to design and manage enterprise knowledge architecture, including ontology design, knowledge graphs, and retrieval, to make domain knowledge structured, governed, and usable by AI systems. * Evaluation and Reliability: Proven ability to build multi-layered evaluation, from expert benchmarks and rule-based checks to human review and model-based grading, backed by production monitoring. * AI-Assisted Development: Fluency with AI coding tools and practices, applied within a disciplined SDLC with rigorous testing, code review, and CI/CD. * Platform and Infrastructure: Strong knowledge of LLMOps, secure cloud deployment (AWS, Azure, or GCP), and observability. Clinical Development Domain * Protocol and Trial Execution: Experience with clinical protocol development, study design, or clinical trial operations. Familiarity with ICH M11 structured protocols, CDISC standards, or USDM is a strong plus. * Regulated AI: Working knowledge of GxP, computer system validation, and emerging regulatory guidance on AI in drug development. Leadership and Collaboration * Proven ability to act as tech lead, owning product direction and delivery while mentoring senior engineers. * Strong partnership with clinicians, medical writers, statisticians, and clinical operations, translating complex AI concepts for research, regulatory, and executive audiences. * Deep commitment to data privacy, security, and responsible AI in development and deployment., Design, implement, and operate production-quality AI framework components, benchmark and optimize large model training and inference across GPUs and Microsoft hardware, build automation and observability, drive cross-team projects from definition to deployment, and provide technical leadership and mentorship for improvements in performance, reliability, and model onboarding velocity., Design, implement, and ensure quality of systems stack, tools, and SDK integration for AI frameworks. Collaborate with hardware and ML teams, apply engineering principles, write low-level/high-performance code, define architecture, estimate scope, and drive coding standards. Top Skills: AzureCC#C++DmaDriver/Kernel InterfacesEbpfGpuObservability ToolsPciePerformance CountersPythonRdmaRustSdkTelemetry HooksTpu ## Description We are seeking a Sr. Principal Software and AI Engineer to serve as the senior technical authority for AI-powered applications across drug development, with a primary focus on protocol generation and clinical trial execution. In this hands-on senior technical leadership role, you will help shape the R&D AI strategy and set technical direction for the engineering team, while building critical components yourself and setting best practices for AI-native development within a disciplined, validated SDLC. You will architect and ship production-grade agentic applications and platforms, set standards for evaluation, observability, and GxP compliance, and raise the bar for engineers through design reviews and mentorship., * Build AI and Agentic Applications: Architect, build, and deploy AI and agentic powered applications that accelerate protocol generation and clinical trial execution, from study design through operational delivery. * Collaborate Broadly: Partner with clinicians, medical writers, statisticians, data scientists, clinical operations, and product teams to embed AI into regulated clinical development workflows. * Shape Architecture: Define scalable, reliable platform architecture for multi-agent orchestration, model and tool integration, and the full LLMOps lifecycle. * Set Standards: Establish best practices for AI-native development, evaluation, testing, observability, and continuous performance monitoring within a validated SDLC. * Drive Innovation: Assess frontier and open-weight models, agent frameworks, and tooling, and make sound build, buy, and model sourcing decisions. * Ensure Compliance: Uphold GxP, data privacy, security, and responsible AI standards, with traceability, auditability, and human oversight built into every system. Supervisory Responsibilities: * Act as tech lead with full ownership of product development, accountable for technical direction, delivery, and quality from concept through production. * Lead project execution, setting priorities, scope, and timelines for the AI engineering team. * Mentor AI and software engineers through design and code reviews, reference implementations, and hands-on guidance. * Lead cross-functional technical workstreams with clinical, medical writing, biostatistics, and clinical operations partners.