Sr. AI Architect - Oaks, PA

Role: Sr. AI Architect Location: Oaks, PANeed candidate who can work onsite from Day 1 (Hybrid)Key Responsibilities Define and own the enterprise AI/ML vision, roadmap, and long-term strategy aligned with business goalsLead design, development, deployment, and lifecycle management of AI and machine learning solutionsBuild and mentor high-performing AI, data science, and ML engineering teamsPartner with Product and Business leaders to identify high-impact AI use cases and prioritize initiativesEstablish best practices for model development, validation, monitoring, explainability, and retrainingEnsure AI solutions comply with data privacy, security, regulatory, and ethical AI guidelinesOversee AI platform architecture, model pipelines, and MLOps frameworks for scalability and reliabilityDrive adoption of generative AI, predictive analytics, NLP, and advanced modeling techniques where applicableCommunicate AI strategy, performance, and risks clearly to executive leadership and stakeholdersEvaluate and manage AI vendors, tools, platforms, and cloud servicesRequired Qualifications Experience with Financial services, Wealth management is preferred to have.Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or related field15+ years of experience in data science, machine learning, or advanced analytics5+ years in a Architecture or people-management role overseeing AI/ML teamsStrong hands‑on experience with ML models, statistical methods, and AI frameworksExperience deploying AI solutions in production environments at scaleHands on RAG experience: embeddings, retrieval strategies, chunking, metadata/routing, evals, and guardrails.Open source frameworks: strong with Lang Chain (or similar), plus experience with FastAPI/Flask and async patterns.Azure: practical experience with Azure OpenAI/Models, Azure AI Search, Azure ML, AKS/Container Apps, Key Vault, App Insights/Log Analytics, and Hybrid Private Networking.Terraform: modules, CI/CD integration, and handling nested data structures.MLOps/Dev Ops: Docker/Kubernetes, CI/CD (Gitlab or Azure Dev Ops), secrets management, and automated testing.Solid understanding of LLMs (prompting, function/tool calling, structured outputs, rate limiting, token/cost management).Proficiency with Python, SQL, and modern data/ML platforms (cloud-based preferred)Strong understanding of data governance, model risk management, and responsible AI practicesExcellent communication skills with the ability to translate complex AI concepts for non-technical audiencesPreferred / Nice-to-HaveExperience in financial services, healthcare, life sciences, or other regulated industriesExposure to generative AI, large language models (LLMs), and prompt engineeringFamiliarity with MLOps tools, CI/CD for ML, and cloud platforms (AWS, Azure, or GCP)Experience driving enterprise AI transformation or center-of-excellence modelsPrior ownership of AI compliance, audit, or regulatory reviews#J-18808-Ljbffr

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