Location : Plymouth Meeting HQ (Hybrid) or Remote
Company Overview :
Braeburn is dedicated to delivering solutions for people living with the serious consequences of opioid use disorder. At Braeburn, we challenge the status quo and champion transformation of the management of opioid use disorder (OUD) by partnering with the community to create a world where every person with OUD gets the best possible care and opportunity to reach their full potential. Our shared commitment to innovation on behalf of patients enables us to help people with OUD begin and sustain recovery. At Braeburn, there are opportunities to contribute to our purpose every day. We value authenticity and strive to amplify all voices. Our culture empowers everyone to be successful and unleashes our full potential.
Position Summary :
We are seeking a highly skilled AI Architect to lead the enterprise-wide design and implementation of cutting-edge AI and orchestrated multi-step workflows. This role is responsible for translating business challenges into robust, scalable, and secure AI architectures. This individual will need to collaborate with cross-functional teams, including functional stakeholders and SMEs, to deliver end-to-end AI systems from conception to deployment and lifecycle management.
This role blends strategy and hands-on prototyping and architectural ownership (not day-to-day engineering), requiring expertise in machine learning (ML), data architecture, and cloud technologies. The AI Architect is responsible for developing compliant AI platforms that solve business problems and drive innovation.
Specific Duties :
- Architecture & Roadmap : Own the reference architectures for machine learning and Generative AI, define build-versus-buy, and publish a 12–18-month AI platform roadmap aligned to business priorities.
- Platform & ML Operations / LLM Operations : Establish core services—data pipelines, feature store / model registry, CI / CD for prompts / models, evaluation harnesses, observability (quality, safety, latency, cost), and automated rollback.
- Solution Design : Design end-to-end solutions that combine predictive models, large language models, and integrations with enterprise applications and data sources; ensure grounding, retrieval, and guardrails patterns.
- Governance & Responsible AI : Stand up model risk and ethics guardrails; define evaluation metrics (factuality, hallucination rate, toxicity, bias), documentation, approvals, and live monitoring for drift.
- Cloud & Infrastructure : Architect for AWS, Azure, and / or GCP and / or on-premise, with secure networking, secrets management, and cost controls.
- Integration : Partner with enterprise / platform teams to integrate AI into workflows, APIs, and user interfaces; ensure identity, authorization, auditability, and reliability SLAs.
- Technical Leadership : Provide architectural governance, conduct design reviews, train users, and lead vendor / tool evaluations.
- Innovation & Research : Track and selectively adopt advances in LLMs, agents / orchestrations, vector databases, and evaluation methods; run lightweight proofs to de-risk delivery.
Skills :
Proficiency in Python and SQL.Excellent communication skills to articulate complex technical concepts to diverse audiences and align executives / SMEs with clear trade-offs.Strong problem-solving, adaptability, and learning agility.Strong project management : ability to complete projects timely, plan and prioritize tasks, and keep leadership and stakeholders updated on status.Education / Experience :
Required
Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or related STEM field.8+ years in solution / data / software architecture with 3+ years leading ML / GenAI systems to production.Strong Python and SQL; deep familiarity with at least one major cloud AI / ML stack (AWS, Azure, or GCP).Hands-on experience designing production ML / GenAI systems (RAG, evaluation, monitoring, incident / rollback).Solid grasp of data architecture (batch / stream), CI / CD, containerization (Docker / Kubernetes), and IaC.Proven track record implementing security, privacy, and compliance controls (HIPAA; validation / audit in regulated settings).Life-sciences experience (clinical, PV, commercial, or RWE / RWD), including validation and Part 11 controls.Experience with model / experiment tooling (MLflow, Kubeflow, Weights & Biases), vector stores (pgvector, Pinecone, Weaviate), and orchestration (LangChain, LlamaIndex, Airflow).GPU / accelerator awareness for training / inference (CUDA basics, scheduling) or distributed compute (Ray).Relevant cloud certifications (AWS Solutions Architect, Azure AI Engineer, GCP Professional ML Engineer).What success looks like (6–12 months) :
Published and adopted AI reference architecture and security / validation patterns.One or more GenAI / ML use cases live in production with measurable outcomes (cycle-time reduction, improved accuracy / factuality, cost targets).Operational LLMOps / MLOps : model / prompt registry, automated evaluations, monitoring dashboards, and on-call processes.Documented governance aligned to HIPAA / GxP / Part 11, with audit-ready artifacts and change control.Braeburn is committed to ensuring equal employment opportunity for all qualified applicants regardless of race, color, religion, sex, sexual orientation, gender identity, national origin, military / veteran status, age, disability, or any other category / characteristic protected by law. We encourage all underrepresented backgrounds to apply.
Reasonable Notice
This description reflects the current role and may be updated as Braeburn's needs evolve. Braeburn is an equal opportunity employer.
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