Forward Deployed Engineer – Agentic AI
New
J
JobgetherArtificial Intelligence
Based in the United StatesFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 8+ years of professional IT experience
- Required Skills
- Cloud ComputingCI/CDRESTful APIsSoftware EngineeringLLMGenerative AI
Requirements
- 8+ years of professional IT experience in software engineering, system design, or related disciplines.
- 2+ years of hands-on experience architecting or building GenAI or agentic AI systems using LLM ecosystems (OpenAI, Anthropic, Gemini, Azure AI, or AWS Bedrock).
- Strong background in software engineering, prototyping, and integrating systems with enterprise data and workflows.
- Solid understanding of LLM orchestration, retrieval-augmented generation (RAG), vector databases, prompt engineering, and tool calling.
- Proficiency with at least one major cloud platform (AWS, Azure, or GCP).
- Experience with APIs, integration architectures, and production-readiness practices.
- Knowledge of modern delivery practices including CI/CD, containerization, observability, and DevSecOps.
- Understanding of AI workload economics, token consumption, and inference scaling.
- Experience supporting presales solutioning, customer discovery, or early-stage delivery.
- Ability to lead technical discussions with non-technical stakeholders and translate complex concepts into actionable recommendations.
Responsibilities
- Lead discovery and solution-shaping activities for GenAI, agentic AI, and AI-enabled workflow transformation initiatives.
- Develop early-stage opportunities, define scope, prototype solutions, and establish implementation roadmaps and effort estimates.
- Translate complex business challenges into practical AI solution architectures covering model selection, data access, orchestration, and tool use.
- Build rapid proof-of-concepts and technical prototypes, prioritizing speed, learning, and measurable impact.
- Explain complex AI, architecture, and delivery concepts to diverse stakeholders including executives and engineers.
- Define evaluation frameworks and success criteria covering AI quality, accuracy, latency, cost, and tool-call reliability.
- Ensure solutions incorporate security, compliance, Responsible AI, and production-readiness practices.
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