AI INTEGRATION ENGINEER
AI INTEGRATION ENGINEER
Posted 10 days ago
SENIORITY
Lead
About the role
- Design, develop, and support AI-driven integration solutions on the company's integration platform (MuleSoft), from conceptualization through deployment and post-Go-Live support
- Build and maintain intelligent agents and agentic workflows, including tool calling, orchestration, memory, and guardrail patterns
- Evolve existing point solutions (e.g., direct LLM API connections) into standardized, reusable, evergreen frameworks that are plug-and-play rather than configuration-heavy
- Develop and maintain connections to LLM providers and model routing services; evaluate and recommend models for specific workloads
- Implement document and data extraction pipelines using AI inference, including validation, error handling, and reprocessing frameworks
- Design and implement retrieval patterns (RAG) to ground agents in company and client data, improving reliability and reducing hallucination
- Establish evaluation, observability, and monitoring practices for AI workflows to ensure production reliability, cost control, and measurable business value
- Stay current with the integration platform vendor's agentic offerings and emerging agentic frameworks; assess and adopt where they advance the AI roadmap
- Build reusable tools and services (e.g., MCP servers, standardized connectors) that can be shared across teams and agent implementations
- Collaborate with integration, Salesforce, and operational systems teams to embed AI capabilities into existing business processes
- Partner with business stakeholders and project leads to translate operational requirements into agent and integration designs
- Document solutions, patterns, and standards; contribute to the AI project portfolio and roadmap planning
- Maintain, support, and develop relational and non-relational databases as needed for AI workloads
- Other duties as assigned
- Education and/or Work Experience Required
- Bachelor's degree in Computer Science, Information Systems, or related field, or equivalent practical experience
- Minimum 3–5 years of software development experience, with hands-on experience in enterprise integration
- Demonstrated hands-on experience building LLM-powered applications or agents in production (tool calling, orchestration, prompt engineering, RAG)
- Experience with integration platforms (MuleSoft strongly preferred; IBM Sterling Integrator, Boomi, or similar considered)
- Familiarity with agentic frameworks and emerging agent orchestration and governance offerings in the enterprise integration ecosystem
- Experience working with LLM provider APIs (e.g., OpenAI, Anthropic, Google) and/or model routing platforms
- Experience developing REST APIs and real-time integrations with internal and external systems
- Knowledge of scripting/transformation languages (e.g., DataWeave, Python, JavaScript)
- Strong SQL scripting capabilities (T-SQL/DDL/DML)
- Experience with full software development lifecycle and CI/CD practices
- Minimum 2 years working in an Agile/Scrum environment
- Expertise in tools like JIRA/Confluence, SharePoint, and common program management tools
- Experience with monitoring and observability platforms preferred
- Industry experience within international supply chain, transportation, or logistics preferred
- Strong written and verbal skills, both business and technical
Before you apply
Applying takes about a minute. These four things decide how fast it moves after that.
Your profile is current
It's what we read first. Occupations, seniority and locations matter more than a long history.
Two examples you can talk through
Not a portfolio — just two pieces of work where you can explain the decisions and what you'd change.
A number in mind
What you're on now and what would make you move. We negotiate better when we know both.
Your notice period
Employers plan around it, and it's the question that stalls offers most often.
Once you apply, someone reads it and calls you before anything reaches the employer — usually within two working days.
