Applied AI Engineer

Hibu•King of Prussia, PA

10d•Hybrid

About The Position

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.

Requirements

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
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
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
Agile Scrum Required

Nice To Haves

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
Experience with AI-DLC Preferred

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