Job Title: AI Solution Architect Work Location: Fully remote position Employment Type: Full-time Employment Status: Exempt, salaried Visa sponsorship is not available for this position. Must reside in the United States. We are not accepting applicants for remote workers in California, Illinois, and New York at this time. Defines solution architectures for artificial intelligence systems, including data pipelines, feature stores, vector indices, model endpoints, orchestration, and serving layers Designs LLM -centric patterns (prompting, functional tool calling, memory, grounding, guardrails) and retrieval augmented generation workflows with appropriate evaluation strategies. Engineers’ agentic archit ectures and multi agent handoffs, specific toolkits, planning/ execution loops and safety boundaries for autonomous or semi-autonomous tasks. Selects and integrates cloud services for training, tuning and inference. Integrates AI services with enterprise platforms, and designs adapters, webhooks, and contract test for reliability. Leads proof of concept pilots , validates feasibility, scales succe ssful patterns, and hardens prototypes into production ready services. Defines reference architectures, templates, Software Development Kits and reusable components. Accelerates delivery across multiple product teams and business units. Partners with security to model threats and mitigate risks (prompt injection, data exfiltration, model poisoning, adversarial inputs ). Establishes human workflows for review, override, and continuous improvement. Coaches engineers and data scientists on AI architecture patterns, evaluation methods and deployment practices. Prepares executive ready ar tifacts, communicates tradeoffs , risks, cost models, and ROI, ties technical decisions to outcome based KPIs. Ensures accessibility, localization, and inclusive design considerations for conversational and generative experiences. 5+ years of progressive experience in data engineering, AI/ML engineering, solution architecture or related roles, with at leas t 3-5 years focus ed on enterprise scale AI/ML or generative AI initiatives Proven t rack record designing and delivering end to end AI architecture in medium sized organizations, including reference blueprints, integration patter n s , scalable deployment models, governance frameworks, and migration roadmaps to product AI systems. Deep expertise in modern generative AI and LLMs, including RAG architectures, fine-tuning/PEFT, agentic workflows, prompt engineering, inference optimization, evaluation techniques, and LLMOps / MLOps best practices. 5+ years of h ands-on proficiency with Databricks platform (Delta Lake, Unity Catalog for governance, Mosaic AI/Databricks AI/ BMLflow for ML/GenAI pipelines, Spark for distributed processing) and data lake/ lakehouse architectures for handling structured, semi-structured, and unstructured data at enterprise scale. Strong experience building and optimizing data pipelines , vector search/embeddings, ETL/ELT processes, and data governance in lakehouse environments to support reliable AI training and inference. Advanced programming skills in Python (primary), with working knowledge of SQL, Scala, and modern AI development tools such as Cursor, Claude, Devin AI , Jupyter / Databricks notebooks, and Git for collaborative workflows. Solid understanding of cloud platforms ( Azure preferred), containerization ( Docker/Kubernetes ), CI/CD pipelines, GPU/accelerator optimization, and cost-efficient scalable AI infrastructure. Experience with AI governance, security, compliance (data privacy, ethical AI, model monitoring/observability), risk assessment, and alignment of AI solutions with enterprise standards and regulations. Experience leading cross-functional teams (data scientists, engineers, product, compliance) in PoCs , technology evaluations, AI adoption programs, and stakeholder presentations to executive audiences. Familiarity with emerging AI trends, hybrid/multi-cloud setups, and tools for rapid prototyping/iteration in enterprise settings. Bachelor's or Master's degree in Computer Science , Data Science, AI, or a related quantitative field Strong communication and strategic skills to translate complex technical architectures into business value for C-level stakeholders. Successfully pass background check for cybersecurity site access. Architects AI solutions from discovery through production that result in mea surable business value. Translates business problems into reference architectures spanning data ingestion, model development and selection, machine learning operations, security and governance, and system integration Designs patterns for LLM/RAG/agentic workflows, event driven and real time use cases and multimodal experiences. Handles build vs buy anal yses, vendor and platform evaluations, and proof of concepts; confirms observability for safety, reliability, d rift, basis and performance. Partners with product, data science, engineering, cloud, security and domain stakeholders to deliver solutions that improve outcomes, efficiency and user experience across enterprise functions. Candidate will be responsible for reviewing policies and procedures related to cybersecurity and those relevant to the functions of their role. Candidate is expected to maintain a cybersecure work environment. Must be able to sit and stand for extended periods of time. Must be able to use hands to type, handle products, tools and navigate a computer keyboard. Must be able to view computer screen for extended periods of time. Specific vision abilities required by this job include close vision and distance vision. Medical, Vision, Dental Insurance H Health Savings Account with Employer contributions 401(k) with Employer match Short-term & Long-term Disability Coverage Accidental Death & Dismemberment Coverage Life Insurance Coverage 80 hours of Paid-Time-Off annually Eight paid holidays per year All other benefits required by applicable law Alignment with Corporate Values All Cutsforth employees are expected to perform their work in a manner that exhibits understanding and adherence to the Company Mission and Core Attributes of Cutsforth Employees. Employees in management roles must exhibit continual improvement along Cutsforth’s Leadership Traits. Further, each employee must read and adhere to corporate policies and safety protocols. Learn more about Cutsforth here: Cutsforth.com/About Read our Mission & Values here: Cutsforth.com/Values Equal Employment Opportunity Statement: Cutsforth will not discriminate against any employee or applicant for employment because of race, color, religion, sex, sexual orientation, gender identity, or national origin. Cutsforth will take affirmative action to ensure that applicants are employed, and that employees are treated during employment, without regard to their race, color, religion, sex, sexual orientation, gender identity, or national origin. Such action shall include, but not be limited to the following: Employment, upgrading, demotion, or transfer, recruitment or recruitment advertising; layoff or termination; rates of pay or other forms of compensation; and selection for training, including apprenticeship. Cutsforth agrees to post in conspicuous places, available to employees and applicants for employment, notices to be provided by the provisions of this nondiscrimination clause. For Cutsforth's full Equal Employment Opportunity Policy, click here: EEO Notice to Employees & Applicants

Join our mission to infuse cutting-edge AI/ML/GenAI into pharmacy benefits as an AI Success Specialist Manager. We are looking for an experienced professional with AI and machine learning expertise to join us in applying cutting-edge open-source tools and/or those developed within the company to increase efficiency and reduce friction within the company as well as for our customers. Drive client value, adoption, and retention for AI products by guiding onboarding, optimizing use cases, and fostering strategic partnerships. Act as technical advisors and change agents, ensuring safe, ethical, and high-ROI deployment of AI solutions. Core duties include accelerating time-to-value, managing a cross-functional technical team, and monitoring performance KPIs.

This role reports to the Director of MLE and works closely with Engineering, Data Science, Product, and the Principal SRE. You will influence cross team platform standards and help elevate engineering rigor across ML and infrastructure. In addition to system design, you will mentor engineers on ML reliability, architecture decision making, and operational excellence. Own ML systems architecture Define ML lifecycle standards Push event driven ML integration Design model packaging and deployment strategy Introduce systemic improvements Reduce architectural and data debt Establish testing and QA standards across ML workflows Build a resilient, scalable ML platform that: Trains distributed models at scale Supports event driven feature computation Enables portable model deployment (internal + external) Standardizes ML lifecycle across products Aligns infrastructure to product usage patterns ML Platform Architecture Define and evolve training orchestration standards Batch vs. streaming inference strategy Feature store direction State store patterns and tooling CPU/GPU scaling strategy When to extend current tooling, and when to replace it Define and evolve training orchestration standards Batch vs. streaming inference strategy Feature store direction State store patterns and tooling CPU/GPU scaling strategy When to extend current tooling, and when to replace it Event Driven ML Integration Design feature pipelines as first-class ML system components Integrate queuing and event systems with ML workflows Build reactive retraining triggers Define model drift detection and automated response systems Ensure retraining pipelines are reproducible and fault tolerant Design feature pipelines as first-class ML system components Integrate queuing and event systems with ML workflows Build reactive retraining triggers Define model drift detection and automated response systems Ensure retraining pipelines are reproducible and fault tolerant Model Packaging & Distribution Define model artifact standardization Deterministic builds Dependency isolation Runtime configuration injection Security constraints Version compatibility contracts Define model artifact standardization Deterministic builds Dependency isolation Runtime configuration injection Security constraints Version compatibility contracts ML Observability, Testing & Reliability Standards Define model performance SLIs Drift detection frameworks Data freshness guarantees Latency SLOs Model failure modes Establish standards for: Automated testing of feature pipelines Training pipeline validation Model artifact verification CI/CD workflows for ML systems Safe promotion from experiment to production Work closely with the Principal SRE to integrate telemetry and operational standards across the full stack. Define model performance SLIs Drift detection frameworks Data freshness guarantees Latency SLOs Model failure modes Establish standards for: Automated testing of feature pipelines Training pipeline validation Model artifact verification CI/CD workflows for ML systems Safe promotion from experiment to production Work closely with the Principal SRE to integrate telemetry and operational standards across the full stack. Operational Excellence & On Call You will help define and operate a sustainable ML on call model in partnership with Engineering and SRE. This includes: Clear ownership boundaries between ML systems and infrastructure Incident classification and severity alignment Runbooks for model failures and data drift Postmortem processes focused on systemic improvement Reducing operational toil through automation You are comfortable being accountable for production ML systems, as well as designing systems that make firefighting rare. You will help define and operate a sustainable ML on call model in partnership with Engineering and SRE. This includes: Clear ownership boundaries between ML systems and infrastructure Incident classification and severity alignment Runbooks for model failures and data drift Postmortem processes focused on systemic improvement Reducing operational toil through automation You are comfortable being accountable for production ML systems, as well as designing systems that make firefighting rare. Reduce Data Architecture Debt Evaluate service landscape alignment to product usage Improve or redefine streaming feature architecture Reduce batch rigidity Recommend infrastructure simplifications Evaluate service landscape alignment to product usage Improve or redefine streaming feature architecture Reduce batch rigidity Recommend infrastructure simplifications

The Optum AI Director, Client Scientific Solutions manages an applied research team focused on transitioning AI models from research to production. This role emphasizes operational execution, team leadership, and implementation of best practices for model development and deployment, while contributing to strategic discussions within the scope of assigned client projects. Manage the development and deployment of production-ready models, ensuring adherence to quality and scalability standards. Guide the team in converting research outputs into operational solutions that meet client needs. Work closely with product managers and engineering leads to define technical requirements and prioritize tasks for successful delivery. Introduce and apply innovative AI techniques within client projects to improve model performance and efficiency. Apply ethical AI principles and ensure compliance with organizational standards in all development activities. Implement best practices for model development, testing, and deployment to maintain operational excellence. Coach and develop team members, fostering technical growth and collaboration.