Sr. Technical Solutions Architect

Why you'll love Softchoice:

We are a software-focused IT solutions and services provider that equips organizations to be agile and innovative, and for their people to be engaged, connected, and creative t means moving them to the cloud, helping them build the workplace of tomorrow, and enabling them to make smarter decisions about their technology. By doing these things we help them create success for their customers and their people.

We stand proudly for our people and support their success through career development and advancement. We are recognized and respected for our culture of inclusion and belonging, continuously striving to do what's good for our people and communities.

The impact you'll have:

We are seeking a Senior Technical Solutions Architect - AI to serve as a hands-on, platform-agnostic technical architect for our strategic AI engagements. This person sits at the intersection of customer strategy, applied AI engineering, and modern software delivery. They translate ambiguous business problems into working prototypes, scalable reference architectures, and production-grade solutions across public-cloud hyperscaler AI platforms and sovereign (on-premise / private) AI environments.

The ideal candidate is equally comfortable whiteboarding an agentic architecture with a CIO, writing the proof-of-concept code that proves it works, and guiding a client engineering team through the secure path to production. They are vendor-fluent but vendor-neutral - recommending the right tool for the workload, the data, the risk profile, and the budget.

What you'll do

Solutioning & Architecture

* Design end-to-end AI solutions spanning Generative AI (RAG, CAG, Graph

RAG, fine-tuning, model distillation) and agentic AI (tool-using agents, multi-agent orchestration, MCP-based integrations).

* Architect across all major hyperscaler AI stacks - AWS (Bedrock, Sage Maker, Q), Microsoft Azure (Azure AI Foundry, Azure OpenAI), and Google Cloud (Vertex AI, Gemini) - and recommend the right platform per workload rather than defaulting to a single provider.

* 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 Open Shift AI, Run:ai, and open-source model serving (vLLM, TGI, Ollama) - for clients with data residency, regulatory, IP, or air-gapped requirements.

* Develop reusable reference architectures, decision frameworks, and trade-off analyses (cost, latency, accuracy, governance, sovereignty) that scale across the practice.

Rapid Prototyping

* Build working prototypes - not just slides. Translate client problem statements into functional demos and pilots in days, not months.

* Stand up RAG, CAG, and agentic workflows quickly using frameworks such as Lang Chain / Lang Graph, Llama Index, CrewAI, Auto Gen, Semantic Kernel, and MCP-compliant agent tool chains.

* Integrate vector stores (Pinecone, Weaviate, Milvus, Chroma, pgvector, Open Search), graph stores (Neo4j, Neptune), and hybrid retrieval pipelines as the use case demands.

* Run rigorous, repeatable evals on prototypes (groundedness, faithfulness, latency, cost-per-task, tool-use accuracy) so recommendations are evidence-based.

AI-Native Engineering & Modernization

* Lead solutioning for AI-native software engineering engagements: AI-assisted development, code refactoring at scale, tech debt burndown, legacy modernization, test generation, and documentation regeneration.

* Architect Secure SDLC (SSDLC) practices into every AI-built or AI-assisted codebase - threat modeling, SAST/DAST integration, SBOM generation, dependency hygiene, secrets management, and supply-chain security.

* Advise clients on integrating AI coding agents (Claude Code, Cursor, Git Hub Copilot Workspace, Devin, and others) into their existing SDLC and Dev Sec Ops  tool chains without compromising guardrails.

* Define MLOps / LLMOps / Agent Ops patterns: model and prompt versioning, evaluation pipelines, observability (traces, token usage, drift), guardrails, and human-in-the-loop review.

AI Security

* Conduct AI-specific threat modeling for every solution - covering adversarial inputs, prompt injection, jail breaking, model inversion, training data extraction, and indirect injection via tool outputs or retrieved documents - and translate findings into concrete mitigations in the architecture.

* Design multi-layer guardrail architectures: input sanitization and intent classification, output filtering (PII redaction, toxicity screening, factual grounding checks), content safety policies, and fallback / refusal handling - covering both hosted API models and self-hosted open-weight deployments.

* Enforce least-privilege access control for agentic systems: scope tool permissions, define agent authorization boundaries, audit and log all tool invocations, and ensure agents cannot escalate privileges or exfiltrate data outside approved boundaries.

* Maintain end-to-end AI supply chain security: vet third-party model weights…