Senior Applied AI Engineer / Forward Deployed Engineer
New
Working from anywhere in the U.S.Full-TimeSenior
Salary75 - 85 USD per hour
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Job Details
- Experience
- 5+ years of experience in software engineering, applied AI, or enterprise systems
- Required Skills
- PythonSQLFlaskSharePointFastAPILLM
Requirements
- 5+ years of experience in software engineering, applied AI, or enterprise systems
- Strong Python development experience building production-grade APIs or services
- Hands-on experience with large language model applications
- Experience with Azure OpenAI Service, OpenAI APIs, or similar platforms
- Proven experience designing and building RAG systems
- Strong understanding of prompt design, grounding, context management, and LLM limitations
- Experience integrating with enterprise data sources, APIs, and document systems
- Ability to work directly with business stakeholders and translate needs into working solutions
- Experience working within secure, governed enterprise environments
- Strong communication, systems thinking, and problem-solving skills
- Ability to balance speed, usability, scalability, and maintainability
- Bachelor’s degree in Computer Science, Engineering, Data Science, Information Systems, or a related field (or equivalent experience)
Responsibilities
- Partner with business and technical stakeholders to understand workflows, challenges, and success metrics
- Translate ambiguous problems into clear technical solutions, designs, and delivery plans
- Design and build AI-powered applications using LLMs, APIs, and enterprise data systems
- Develop production-grade backend services using Python and frameworks like FastAPI or Flask
- Build and maintain RAG systems, including document ingestion, chunking, embedding, and hybrid search
- Integrate AI solutions with SharePoint, Microsoft Graph, SQL databases, internal APIs, and business applications
- Design secure systems that respect access control, governance, and enterprise compliance requirements
- Build observable, reliable, and maintainable AI workflows in production environments
- Establish evaluation frameworks for LLM systems to measure accuracy, groundedness, and latency
- Iterate rapidly through prototypes, pilots, and production releases based on user feedback
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