Solution Architect – AI and Automated Solutions

Solution Architecture and Stakeholder Collaboration

Lead discovery sessions to identify high-value use cases for AI and automated solutions, including workflow automation, copilots, knowledge retrieval, and intelligent routing.

Translate business objectives into technical designs covering workflows, data flows, integration points, and non-functional requirements such as latency, reliability, security, and compliance.

AI and Automated Solution Design and Implementation

Design and implement AI Agentic solutions that can reason, plan, call tools, and execute multi-step tasks across APIs, databases, SaaS platforms, and document repositories.

Build solution workflows using patterns such as planner-executor, supervisor-worker, and human-in-the-loop with frameworks including LangChain, LangGraph, and similar orchestration libraries such as LlamaIndex and Semantic Kernel.

Use MCP-style protocols to standardize tool discovery and invocation, including tool schemas, validation, retries, error handling, and safe execution patterns.

Implement retrieval-augmented generation pipelines, including ingestion, chunking, metadata design, embeddings, vector indexing, hybrid search, reranking, and retrieval orchestration.

Apply prompt and policy design techniques, including structured prompting, guarded templates, reasoning aids, and evaluation approaches to improve quality and reduce hallucinations.

Systems Integration and Productionization

Integrate AI and automated solutions into web and mobile apps, core enterprise systems (CRM, Support, ERP, HR, finance, ticketing, supply chain), collaboration tools, and process platforms.

Define and implement guardrails and safety controls, including authorization, policy enforcement, content filtering, PII protection, and safe tool calls.

Work with platform and DevOps teams to deploy using cloud native patterns (containers, serverless, microservices, CI/CD).

Use managed AI and cloud services (e.g., Amazon Bedrock, Amazon SageMaker, and core cloud infrastructure) for orchestration, hosting, and deployment.

Design and extend AI and automated solutions using Microsoft Copilot Studio, including low-code conversational workflows, enterprise connectors, custom actions, and integrations with business systems and Microsoft 365 experiences.

Define and track KPIs (task success, latency, cost, user satisfaction) and use monitoring, logging, tracing, and evaluation to drive continuous improvement and ensure production readiness.