Sr. Technical Solutions Architect

Solutioning & Architecture

• Solutioning & Architecture 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)
• Architect across all major hyperscaler AI stacks — AWS (Bedrock, SageMaker, 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 OpenShift AI, Run:ai, and open-source model serving (vLLM, TGI, Ollama) — for clients with data residency, regulatory, IP, or air-gapped requirements

Development & Modernization

• 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
• Advising clients on integrating AI coding agents (Claude Code, Cursor, GitHub Copilot Workspace, Devin, and others) into their existing SDLC and DevSecOps toolchains without compromising guardrails
• Define MLOps / LLMOps / AgentOps patterns: model and prompt versioning, evaluation pipelines, observability (traces, token usage, drift), guardrails, and human-in-the-loop review

Security

• AI Security
• Conduct AI-specific threat modeling for every solution — covering adversarial inputs, prompt injection, jailbreaking, 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
• Maintain end-to-end AI supply chain security: vet third-party model weights and datasets for backdoors or poisoning, validate fine-tuned model integrity, enforce cryptographic signing of model artifacts, and apply model cards and datasheets as governance artifacts

Client Engagement & Enablement

• 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

Qualifications

• 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
• Demonstrated ability to rapidly prototype AI solutions and ship working code — not just designs or documents
• Deep, hands-on experience 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
• Proficiency in Python; comfort in at least one additional language (TypeScript/JavaScript, Go, Java, or C#)
• 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
• Foundational fluency in AI security concepts: able to identify and articulate risks such as prompt injection, data poisoning, model extraction, and inference-time attacks, and to reason about appropriate mitigations for each in the context of a given architecture and risk tolerance

Preferred Skills

• Software development background with real production experience across the SDLC and Secure SDLC (SSDLC) — including CI/CD, infrastructure as code (Terraform, Pulumi, Bicep), containers and Kubernetes, and DevSecOps tooling
• Experience leading code refactoring, technical debt remediation, and legacy modernization programs — ideally with AI-assisted approaches
• Experience designing sovereign / on-premise AI deployments: NVIDIA NIM / NeMo, OpenShift AI, Run:ai, vLLM at scale, GPU capacity planning, and on-prem vector / graph stores
• Background in security and governance: prompt injection defense, output filtering, data loss prevention, model risk management, NIST AI RMF, ISO/IEC 42001, and EU AI Act readiness; familiarity with the OWASP LLM Top 10, adversarial ML attack taxonomies (MITRE ATLAS), and red-teaming / evaluation techniques for LLMs; experience translating these frameworks into practical control designs rather than checkbox compliance
• Experience fine-tuning, distilling, or post-training open-weight models (Llama, Mistral, Qwen, Gemma) for enterprise use cases
• Industry experience in regulated verticals (financial services, healthcare, public sector, defense) where sovereignty and compliance are non-negotiable
• Relevant certifications (AWS / Azure / GCP AI specialty, CKA/CKAD, CISSP, NVIDIA-certified) — useful, but capability is weighted more heavily than credentials