Position Summary As our Solutions Architect, you’ll serve as the technical and strategic bridge between customers, delivery teams, and executive stakeholders—designing end-to-end architectures that combine modern data platforms, advanced analytics, and applied AI/ML. You’ll own solution design from discovery through handoff, ensuring every engagement is technically sound, financially viable, and positioned for measurable business impact. Key Responsibilities - Design cloud-native data and AI architectures (reference patterns, data models, MLOps pipelines, LLM orchestration) that balance scalability, security, cost, and speed to value. - Translate complex technical concepts into executive-ready narratives, visuals, and roadmaps. - Advise clients on AI strategy, build-vs-buy decisions, governance, and ethical considerations. - Provide hands-on guidance to developers, data engineers, and ML engineers; review code, pipelines, and infrastructure for best-practice adherence. - Conduct architecture reviews and risk assessments; identify and execute course corrections as needed. - Mentor junior architects and consultants; contribute to reusable accelerators, templates, and an internal knowledge base. - Evaluate emerging AI/ML tools, frameworks, and cloud services to keep our stack forward-leaning and competitive. Required Qualifications - 8+ years in solution architecture, data engineering, or software engineering roles; 3+ years architecting AI/ML solutions in production. - Proven success designing large-scale systems on AWS, Azure, or GCP—including data lakes/warehouses, feature stores, MLOps, model monitoring, and CI/CD. - Hands-on experience with at least two of the following: - Deep-learning frameworks (TensorFlow, PyTorch, JAX). - NLP/LLM stacks (Hugging Face, LangChain, vector databases, RAG patterns). - Computer-vision pipelines (OpenCV, TorchVision). - AutoML & orchestration (Vertex AI, SageMaker, MLflow, Kubeflow, Airflow). - Proficiency in Python and at least one typed language (Go, Java, C#). - Solid grounding in data modeling, API design (REST/GraphQL), containerization (Docker, Kubernetes), and IaC (Terraform, CloudFormation, or Pulumi). - Exceptional communication skills—able to whiteboard with engineers in the morning and brief the C-suite in the afternoon. - Bachelor’s degree in Computer Science, Engineering, or related field (or equivalent experience). - Consulting or professional-services experience Bonus Skills - Familiarity with privacy regulations (GDPR/CCPA) and AI governance frameworks (NIST AI RMF, ISO/IEC 42001 draft). - Track record leading GenAI POCs or production deployments (chatbots, copilots, content generation, autonomous agents). - Relevant certifications (e.g., AWS Solutions Architect Professional, Google Professional Cloud Architect, Microsoft Azure Solutions Architect, TensorFlow Developer). Core Competencies - Architectural Systems Thinking – Visualizes complex interactions across data, application, and infrastructure layers. - AI/ML Depth & Breadth – Stays current on techniques, tooling, and responsible-AI best practices. - Client Influence – Builds trust quickly; frames solutions around tangible ROI. - Execution Leadership – Drives estimation accuracy, risk mitigation, and delivery quality. - Collaboration & Mentoring – Elevates team capability through coaching and knowledge sharing.