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The Applied AI Engineer is a hands-on technical role responsible for rapidly prototyping and validating GenAI, Agentic AI, and ML-powered solutions across Hibu's business units. Based in the central AI/Enterprise Architecture team and deployed on rotational engagements with Sales & Marketing, Operations, and Product teams, this role translates business problems into working AI proof-of-concepts within weeks, not months. The Applied AI Engineer works primarily at the AI platform integration layer-orchestrating foundation models via AWS Bedrock, building agentic workflows with MCP (Model Context Protocol), designing prompt systems, and wiring AI capabilities into enterprise platforms such as Salesforce. Once a POC is validated, this role pairs with the receiving engineering team to transition the solution into production. The position requires equal comfort writing production-quality code, facilitating business stakeholder workshops, and navigating ambiguity in rapidly evolving AI technology landscapes. 

Primary Responsibilities:

•  Partners with business and engineering teams to identify high-value AI use cases and translate them into scoped proof-of-concept projects 
• Rapidly builds and iterates on GenAI and Agentic AI prototypes-targeting POC delivery in 2-4 weeks-using foundation models, prompt engineering, and agentic orchestration patterns
•  Designs and implements agentic AI workflows leveraging Model Context Protocol (MCP) for enterprise integrations with Salesforce, marketing automation systems, customer data platforms, and internal tools 
•  Builds on Hibu's AI platform stack including AWS Bedrock, Claude/GPT model orchestration, prompt system design, RAG pipelines, and API-first integration patterns 
• Pairs with receiving engineering teams (2-3 sprints) to transition validated POCs into production, including architecture documentation, pair-coding sessions, and knowledge transfer
•  Evaluates emerging AI technologies, frameworks, and tools (e.g., AWS Strands Agents, LangGraph, CrewAI) and recommends adoption based on feasibility, cost, and alignment with Hibu's platform strategy 
• Creates concise POC documentation including solution architecture, prompt designs, integration patterns, success criteria, and transition plans for engineering handoff
•  Presents POC results, demos, and recommendations to both technical and business audiences, translating AI capabilities into business value language
•  Contributes learnings, reusable components, and best practices to Hibu's AI & Automation Hub and internal knowledge base 
• Adheres to AI governance guidelines, responsible AI practices, data privacy protocols, and security standards (OWASP LLM Top 10, NIST AI RMF) during all prototyping and integration work
• Stays current with AI/ML trends through continuous learning, experimentation, and active engagement with foundation model releases, agentic AI developments, and enterprise AI integration patterns

Competencies & Critical Skills:

•  Production-level proficiency in Python and/or TypeScript with ability to rapidly prototype and ship working AI applications 
•  Hands-on experience building LLM applications: prompt engineering at system level, RAG pipelines, agentic workflows (LangChain, LangGraph, or equivalent frameworks)
• Strong working knowledge of foundation models (Claude, GPT, Gemini) and their enterprise integration patterns
• Experience with cloud AI platforms (AWS Bedrock preferred; Azure AI or Google Vertex AI acceptable) and API orchestration
• Understanding of Model Context Protocol (MCP) or similar AI-system integration patterns (tool use, function calling, agent-to-service communication)
• Strong communication skills with ability to run business stakeholder discovery sessions, present POC demos, and explain AI tradeoffs without jargon 
• Comfort with ambiguity-can walk into a new business team, listen to unstructured problems, and define a scoped POC independently
• Strong handoff discipline-documents architecture decisions, writes transition guides, and pairs effectively with receiving engineering teams
• Innovative mindset with ability to experiment quickly, fail fast, and iterate toward effective AI-native solutions

Experience and Qualifications:                                                                                                                                                                                                                                             Required/ Preferred:

• Bachelor's Degree in Computer Science, Software Engineering, AI/ML, or related field                                                                                                                                                     Required
• 5-8 years of software engineering experience with at least 2 years focused on AI/ML application development                                                                                                                Required
• Hands-on experience building LLM-powered applications (RAG, prompt engineering, agentic workflows) shipped to real users or business stakeholders                                             Required
• Production-level proficiency in Python and/or TypeScript for rapid prototyping and application development                                                                                                                     Required
• Experience with cloud AI platforms (AWS Bedrock preferred; SageMaker, Azure AI, or Vertex AI acceptable)                                                                                                                   Required
• Strong API integration skills (REST, webhooks, MCP or similar agent-to-service patterns)                                                                                                                                                 Required
• Experience with agentic AI frameworks/platforms (LangChain, LangGraph, CrewAI, AWS Strands Agents, Claude Managed Agents, AWS Agentcore or equivalent)                            Required
• Salesforce ecosystem familiarity and CRM integration experience                                                                                                                                                                                      Preferred
• Familiarity with AI evaluation frameworks, prompt testing, and LLM output quality assurance                                                                                                                                            Preferred
• Experience in digital marketing technology, SMB SaaS platforms, or sales/marketing operations                                                                                                                                      Preferred
• Demonstrated ability to facilitate business discovery sessions and present POC demos to non-technical stakeholders                                                                                                    Required
• Experience pairing with engineering teams to transition prototypes into production systems                                                                                                                                              Required
• Experience with AI-DLC                                                                                                                                                                                                                                                        Preferred
• Agile Scrum                                                                                                                                                                                                                                                                           Required

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