Applied AI Engineer/Architect

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Applied AI Engineer/Architect

West Monroe is seeking an Applied AI Engineer/Architect to lead the design and delivery of enterprise AI solutions that solve complex business challenges. This role serves as the technical AI leader across client engagements and West Monroe assets, defining AI architectures, guiding implementation teams, and helping clients make informed decisions about when to leverage large language models (LLMs), deterministic automation, platform-native AI capabilities, or traditional software engineering approaches. As a Senior Architect, you will provide deep technical expertise, contribute to business development efforts, and help advance West Monroe’s AI capabilities through reusable assets, thought leadership, and mentorship.

What You Will Do

Client Delivery

• Lead the architecture and delivery of enterprise AI solutions, including model selection, retrieval strategies, workflow orchestration, evaluation frameworks, observability, guardrails, and human oversight.
• Design and implement AI-enabled applications using LLMs, agents, retrieval augmented generation (RAG), tool integrations, and secure production deployment patterns.
• Define semantic models, ontologies, business objects, metadata strategies, permissions, and data contracts that enable reliable AI reasoning and enterprise workflows.
• Advise clients on AI strategy, technical feasibility, data readiness, model selection, governance, responsible AI practices, and implementation roadmaps.
• Partner with application, data, and cloud engineering teams to integrate AI capabilities into production systems, APIs, data platforms, security architectures, and operational workflows.
• Establish AI evaluation methodologies using benchmark datasets, regression testing, red-team scenarios, quality metrics, and release criteria to ensure reliable and measurable outcomes.
• Leverage AI tools to accelerate analysis, synthesize complex information, and support data-driven recommendations for clients, exercising sound judgment in evaluating outputs.
• Apply AI technologies (e.g., generative AI, automation tools, data models) to enhance insights, improve delivery efficiency, and elevate the quality of client outcomes.

Business Development

• Serve as a trusted technical advisor during pursuits, helping shape AI strategy, solution architecture, and delivery approaches for complex client opportunities.
• Support proposals, client presentations, and executive discussions by articulating AI capabilities, tradeoffs, risks, and business value.
• Build credibility in the marketplace through deep expertise in enterprise AI architecture and emerging technologies.

Practice Development

• Develop reusable AI frameworks, accelerators, evaluation methodologies, and reference architectures that strengthen West Monroe’s AI offerings.
• Mentor engineers and architects while sharing technical expertise across project teams.
• Stay current on frontier AI models, enterprise agent architectures, hyperscaler AI platforms, governance expectations, and secure deployment practices.
• Contribute to thought leadership, internal capability building, and innovation initiatives that expand West Monroe’s Applied AI practice.

What You Will Bring

• 7+ years of experience in software engineering, data engineering, machine learning, applied AI, or technical architecture, including recent hands-on AI engineering experience.
• Proven experience building production AI solutions using LLMs, AI agents, RAG, prompt engineering, AI evaluation frameworks, observability, MLOps, or machine learning platforms.
• Strong Python development skills and proficiency in at least one additional enterprise technology stack such as TypeScript, Java, C#, or modern data engineering frameworks.
• Experience with one or more leading AI ecosystems, including Azure OpenAI, AWS Bedrock, Google Vertex AI, Databricks, Snowflake, Microsoft Fabric, or comparable platforms.
• Strong understanding of cloud architecture, APIs, identity and access management, security, data engineering, application integration, and production deployment practices.
• Knowledge of semantic modeling, ontology design, knowledge representation, data governance, and enterprise AI architecture.
• Ability to communicate AI concepts, technical tradeoffs, and implementation risks effectively to executive stakeholders, business leaders, architects, and engineering teams.
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