Senior AI Solutions Architect

Job Title: Senior AI Solutions Architect

Location: Peoria, IL, 61602, USA

Duration: 12 months contract.

Must Have Skills/Attributes:

• AI, AWS, Cloud, GCP, Generative AI/GenAI, Microservices, Python, RAG (Retrieval-Augmented Generation), REST.

Experience Desired:

• Enterprise AI & Generative AI Solution Architecture (LLMs, RAG, Vector Search, Agentic AI, NLP) (5+ yrs).
• Enterprise Solution Architecture across Hybrid Cloud (Azure, AWS, or GCP) (8+ yrs).
• Business Discovery, Requirements Gathering & Solution Design (8+ yrs).
• Cloud-Native Architecture (APIs, Microservices, Containers, Enterprise Security) (5+ yrs).
• AI Implementation Leadership using Python, REST APIs, SDKs, and Reference Implementations (5+ yrs).
• Required Minimum Education: Bachelor’s Degree.

JOB DESCRIPTION:

Required Education/Experience:

• Bachelor’s degree in Engineering or IT.

Required Qualifications:

• This requisition is for an AI Solution Architect contractor to support existing CLIENT architects in shaping and defining business-driven enterprise AI and data use cases, including Generative AI
• The resource will participate in business discovery, translate use cases into scalable solution designs, and provide architectural guidance to internal development teams across hybrid enterprise environments.

Preferred Qualifications:

• Experience with specific technologies, platforms, frameworks, or specialized approaches such as Azure OpenAI, Amazon Bedrock, Vertex AI, AI Refinery, Microsoft Foundry, LangChain, LangGraph, GraphRAG, Snowflake, Dataiku, Agent Skills/agent harness patterns, MCP servers, and similar enterprise AI and data platforms.
• Experience with container platforms, orchestration technologies, or scalable deployment patterns such as Kubernetes, AKS, EKS, or similar enterprise hosting environments.
• Experience developing reusable architecture patterns, reference implementations, or solution accelerators to support adoption across multiple teams or business use cases.

Required Technical Skills:

• Proven experience designing and implementing enterprise AI and Generative AI solutions, including familiarity with enterprise AI platforms, model integration patterns, solution architecture considerations for production use cases, and modern AI solution approaches such as NLP, RAG, vector-based search, enterprise search, prompt orchestration, agentic AI patterns, multi-agent workflows, and orchestration concepts.
• Experience facilitating discovery sessions, gathering business and functional requirements, and translating them into solution architecture recommendations and implementation considerations.
• Experience designing scalable enterprise solutions across on-premises and cloud environments, including familiarity with distributed computing platforms such as Microsoft Azure, AWS, and/or Google Cloud Platform (GCP), and modern application and integration patterns such as APIs, microservices, containerized workloads, cloud-native architectures, and enterprise identity/security integration.
• Experience guiding internal development teams through implementation planning and solution alignment, including the ability to create reference implementations or lightweight prototypes and apply programming languages and common tools used in enterprise AI and cloud solution development.
• Ability to create reference implementations or lightweight prototypes to help internal development teams understand and apply recommended solution patterns for new use cases.
• Working knowledge of programming languages and common tools used in enterprise AI and cloud solution development, such as Python, JavaScript/TypeScript, REST APIs, SDKs, notebooks, and containerized application patterns, sufficient to create reference implementations, review technical approaches, and guide internal development teams.
• Strong communication, facilitation, and collaboration skills, including the ability to work effectively with both business stakeholders and technical teams.

Position’s Contributions to Work Group:

• This role is intended for someone who can quickly engage with architects, business stakeholders, and internal development teams to assess needs, evaluate solution options, and define practical solution approaches for enterprise AI and data use cases, including Generative AI.
• Success in this role requires strong discovery, solution architecture, and cross-team collaboration skills.
• The role operates across hybrid on-premises and cloud environments and supports solution definition using approved enterprise platforms, services, and architectural patterns.
• It is primarily focused on discovery, solution architecture, and implementation guidance rather than hands-on model development or day-to-day application coding.

Typical task breakdown:

• Partner with existing architects and technical teams to shape solution approaches for new and evolving business use cases.
• Participate in discovery sessions with business stakeholders to understand objectives, pain points, constraints, and desired outcomes for proposed use cases.
• Translate business and functional requirements into solution concepts, architecture recommendations, and implementation considerations that align with enterprise standards.
• Collaborate with cross-functional teams, including architects, internal development teams, engineers, data scientists, machine learning engineers, and domain experts, to refine solution direction, provide implementation guidance, and support delivery planning.
• Create or demonstrate reference implementations or lightweight prototypes, when appropriate, to illustrate recommended approaches for new or evolving use cases.
• Assess solution options, tradeoffs, dependencies, and technical feasibility for proposed business use cases across hybrid enterprise environments.
• Facilitate alignment between business stakeholders and technical teams by clarifying use case scope, assumptions, constraints, and recommended next steps.
• Support evaluation and recommendation of approved tools, platforms, and solution patterns needed to enable business use cases in alignment with enterprise architecture and governance standards.
• Create clear documentation for proposed solutions, including architecture views, assumptions, dependencies, design considerations, and recommended implementation approaches.
• Contribute to reusable architecture guidance, design patterns, and solution documentation that can support repeatable delivery across similar AI and data use cases.