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.