AI Specialist – Applied AI

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BPM LLP

WebsiteLinkedInAll Job Openings

501 - 1000 employees

💸 Finance

💼 Consulting

☁️ SaaS

Finance • Consulting • SaaS

BPM LLP is a large California-based accounting and consulting firm providing a wide range of services. These include assurance, advisory, tax, and IT consulting, with specialized expertise in areas like cybersecurity and risk advisory. The firm serves a diverse client base across multiple industries, including financial services, real estate, and technology. BPM is committed to delivering personalized service tailored to meet the specific needs of each client, facilitating business growth and compliance with industry regulations.

📋 Description

• Scope, design, and deliver applied AI solutions, including LLM workflows, RAG systems, agentic automation, and model evaluation, integrated into client and business systems • Work directly with clients through discovery, solution design, demos, and delivery • Translate business problems into AI solutions suitable for production • Define patterns and specifications for the prompt-development pod • Review and guide production builds • Establish evaluation criteria and measure solution quality, accuracy, and ROI • Advise clients on AI readiness, data, and responsible, safe deployment • Contribute to reusable AI accelerators and go-to-market offerings

🎯 Requirements

• 4+ years in AI/ML, data science, or applied AI engineering • Hands-on experience with LLMs in production or near-production settings • Strong Python and modern AI tooling skills • Experience with RAG and embeddings/vector stores • Experience with at least one major LLM provider or framework • Client-facing ability to lead working sessions, explain trade-offs to non-technical stakeholders, and manage scope • Fluent English • Consistent overlap with US business hours • Consulting or professional-services background preferred • Exposure to ERP/CRM platforms such as NetSuite or Salesforce, or finance/operations data, preferred • Experience with evaluation, observability, or guardrail frameworks preferred • Cloud and MLOps familiarity preferred