Role Description
Must-Have Skills
- Strong AI/ML engineering and software development experience
- Expertise in LLMs, Gemini APIs, Vertex AI, and Google Cloud
- Experience building agentic AI workflows, copilots, and intelligent agents
- Hands-on implementation of RAG systems and vector databases
- Strong prompt engineering skills and applied analytics knowledge
- Ability to integrate AI solutions with enterprise systems
- Experience with middleware, orchestration layers, and API integrations
- Knowledge of AI observability, evaluation frameworks, guardrails, and security controls
- Strong problem-solving, debugging, and rapid prototyping ability
- Excellent communication and stakeholder management skills
- Ability to thrive in fast-paced, ambiguous environments with ownership mindset
Key Responsibilities
* Develop and deploy AI solutions using Gemini Enterprise, Vertex AI, and Google Cloud * Build prototypes, POCs, and production-grade AI applications rapidly * Design and implement agentic workflows, RAG systems, and AI orchestration pipelines * Integrate LLMs with enterprise platforms and build middleware layers * Create intelligent copilots, multi-modal demos, and automation workflows * Ensure AI systems meet standards for reliability, s ecurity, explainability, and compliance * Implement monitoring, evaluation, and human-in-the-loop frameworks * Work directly with clients to gather requirements and design AI solutions * Lead technical discussions, discovery workshops, and solution demos * Act as a bridge between business stakeholders and engineering teams Location Milford Center, OH Job Function TECHNOLOGY Role Senior Engineer Job Id 416664 Desired Skills Artificial Intelligence Salary Range $120,000-$160,000 a year
About Forward Deployed Engineering
Forward Deployed Engineers are embedded directly with customers to build custom solutions, integrate products into existing infrastructure, and bridge the gap between product engineering and customer success. The role combines deep technical skills with the ability to operate in client environments and translate business requirements into working software.
Originally pioneered by Palantir, the FDE model has spread across AI, enterprise SaaS, and cloud infrastructure companies. FDEs write production code, architect integrations, train customer teams, and feed product insights back to the core engineering organization. At companies like OpenAI, Salesforce, and Databricks, FDE teams are treated as elite engineering units that can ship custom solutions in days rather than quarters.
Typical FDE stack: Python, TypeScript, SQL, REST/GraphQL APIs, cloud platforms (AWS/GCP/Azure), and increasingly LLM APIs and AI orchestration frameworks. Strong communication and the ability to context-switch between technical and business conversations are as important as coding ability.
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