Solutions Architect

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.