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successfactors

Sr. Technical Solutions Architect, AI/Data

Softchoice
Toronto, CAN
Senior · 8+ years experience
Onsite
Discovered 4 days ago
awsazurebigquerydatabricksgcpgraphql

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What You'll Do

  • Solutioning & Architecture Architect sovereign / on-premise AI solutions using stacks such as NVIDIA AI Enterprise (NIM, NeMo, Blueprints), Dell AI Factory, HPE Private Cloud AI, Red Hat OpenShift AI, Run:ai, and open-source model serving (vLLM, TGI, Ollama) — for clients with data residency, regulatory, IP, or air-gapped requirements. Design end-to-end AI solutions spanning Generative AI (RAG, CAG, GraphRAG, fine-tuning, model distillation) and agentic AI (tool-using agents, multi-agent orchestration, MCP-based integrations). Understanding of hybrid architecture across all major hyperscaler AI stacks — AWS (Bedrock, SageMaker, Q), Microsoft Azure (Azure AI Foundry, Azure OpenAI), and Google Cloud (Vertex AI, Gemini) Develop reusable reference architectures, decision frameworks, and trade-off analyses (cost, latency, accuracy, governance, sovereignty) that scale across the practice. Client Engagement & Enablement Serve as the senior technical voice in client conversations — from executive briefings through deep technical design sessions. Partner with sales, delivery, and practice leadership to scope statements of work, estimate effort, and de-risk delivery. Mentor architects, engineers, and consultants across the broader AI practice; raise the technical bar through code reviews, internal enablement, and reusable assets. Stay ahead of the field — evaluate emerging models, frameworks, and protocols (e.g., MCP, A2A, ACP, new agent frameworks, new sovereign AI stacks) and bring well-reasoned points of view back to the practice.

What You'll Bring

  • To The Table: 8+ years of progressive experience in software engineering, solutions / Enterprise architecture, or applied AI/ML, with at least 2+ years in a hands-on Generative AI or agentic AI role. Professional fluency in French and English, written and spoken (required) — able to lead client workshops, executive presentations, and technical documentation in both languages. Experience designing sovereign / on-premise AI solutions, including private model serving (NVIDIA NIM / NeMo, vLLM, TGI, Ollama), enterprise AI platforms (NVIDIA AI Enterprise, Red Hat OpenShift AI, Dell AI Factory, HPE Private Cloud AI, Run:ai), and on-premise vector / graph stores. Solid understanding of the infrastructure behind on-premise AI: GPU architecture and capacity planning, inference optimization (quantization, batching, KV-cache management), high-performance networking and storage, and Kubernetes-based GPU scheduling. Strong understanding of data sovereignty requirements and controls — data residency, privacy regulation (e.g., PIPEDA, Quebec Law 25, GDPR), air-gapped and disconnected deployments — and the ability to articulate trade-offs between sovereign, hybrid, and public-cloud AI architectures. Deep knowledge with at least one of the three major hyperscaler AI platforms (AWS, Azure, GCP) and a working understanding of the second and third. Production experience designing and shipping RAG and/or agentic systems, including practical familiarity with chunking strategies, embedding model selection, retrieval evaluation, and orchestration patterns. Working knowledge of MCP (Model Context Protocol) and modern agent-tool integration patterns; ability to design MCP servers and clients, and to reason about when MCP is the right abstraction versus alternatives. Strong understanding of CAG (Cache-Augmented Generation), RAG variants (naive, hybrid, GraphRAG, agentic RAG), and the trade-offs between each. Experience integrating with enterprise systems: REST/GraphQL APIs, event streams (Kafka, EventBridge), identity (OIDC, SAML, OAuth2), and enterprise data platforms (Snowflake, Databricks, Fabric, BigQuery). Excellent written and verbal communication; able to move fluidly between executive narrative and engineering whiteboard. Education Bachelor's degree in Computer Science, Engineering, Mathematics, or a related technical field, or equivalent demonstrable experience. Advanced degree is welcomed but not required. Location & Travel Remote-friendly with periodic travel to client sites and internal events (estimated 0–15%).

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