Solutions Architect

Position Summary As our AI Solutions Architect , you’ll serve as the technical and strategic bridge between customers, delivery teams, and executive stakeholders for our AI engagements. You will work closely with the product and analyst team, to deeply understand the business gap/ opportunity, user profile and technology landscape, and act as the single point of design authority on the production solution design. You will ensure all technical designs create a coherent, scalable, integrated compelling solution, and you will work closely with the Product Leadership to balance technical and solution trade-offs and priorities. You will lead the design of end-to-end integration-first AI solutions, with a strong focus on connecting AI-enabled products, cloud services, and data platforms. 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 AI and data solution architectures, including reference patterns, data flows, AI/ML workflows, and LLM orchestration that balance scalability, security, cost, and speed to value. Translate AI and data architectures into clear diagrams, executive-ready narratives, visuals, and roadmaps for technical and non-technical stakeholders. Advise clients on AI strategy, build-vs-buy decisions, governance, and ethical considerations. Integrate and orchestrate data across multiple platforms, including cloud data warehouses and lakes (e.g., Snowflake, BigQuery, Databricks) and external APIs Collaborate with ML Engineers and Data Engineers to validate architectural alignment and integration feasibility. Conduct architecture reviews and risk assessments; identify and execute course corrections as needed. Architect AI solutions that leverage LLMs and RAG patterns to deliver interpretable insights and human-readable outputs from complex model results. Mentor junior architects and consultants; contribute to reusable accelerators, templates, and an internal knowledge base. Provide technical oversight across multiple client engagements, ensuring architectural consistency, delivery quality, and alignment between business requirements and implemented solutions. Provide hands-on guidance to engineering and data teams on API integrations, data pipelines, and AI service connectivity , ensuring solutions are scalable, secure, and maintainable. Stay current on emerging AI platforms, LLM tooling, and cloud-based data services , continuously evaluating how they can enhance our integration capabilities. Required Qualifications 8+ years in solution architecture, data engineering, or software engineering roles; 3+ years architecting AI/ML solutions in production. Proven experience designing modern, cloud-native AI solutions and integrations across AWS, Azure, or GCP, including data platform integration, LLM orchestration, and secure API connectivity. 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). 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.