Sr. Technical Solutions Architect

Solutioning & Architecture

• Solutioning and 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

Prototyping & Development

• 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 LangChain / LangGraph, LlamaIndex, CrewAI, AutoGen, Semantic Kernel, and MCP-compliant agent toolchains
• Integrate vector stores (Pinecone, Weaviate, Milvus, Chroma, pgvector, OpenSearch), 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

Engineering & 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
• Advises 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 & Compliance

• 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
• Align AI solutions to applicable compliance frameworks — NIST AI RMF, OWASP LLM Top 10, ISO/IEC 42001, EU AI Act, and relevant sector-specific regulations — and produce the risk documentation, impact assessments, and audit trails clients need to satisfy internal governance and external regulators

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