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# Senior AI/ML Engineer

- **Company:** Jobot
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $160,000.0 - $200,000.0
- **Contract:** Permanent contract
- **Skills:** .NET Framework, Application Programming Interfaces (APIs), Artificial Intelligence, Microsoft Azure, Cloud Storage, Continuous Integration, JSON, Python (Programming Language), Machine Learning, Microsoft SQL Server, Octopus Deploy, OpenAI, Search Technologies, Enterprise Search, Retrieval-Augmented Generation, Transfer Learning, Large Language Models, Prompt Engineering, Kubernetes, Structured Output Generation, Cosmos DB, Machine Learning Operations, Azure AI
- **Published:** October 18, 2026
- **Apply:** https://www.dice.com/job-detail/652f9892-e031-4acd-8d19-4d4063983421

## About the Role

* 5+ years in applied ML, including experience with retrieval, embeddings, and prompt engineering
 * Strong Python skills and familiarity with production-grade ML pipelines
 * Experience designing and tuning RAG workflows with hybrid search
 * Familiarity with RLHF and fine-tuning on structured JSON output
 * Solid grasp of system-level thinking-how to bring ML into product environments cleanly

Nice to have: .NET understanding, especially for integration and orchestration layers

## Description

New US based .NET team tasked with making an existing, modern platform utilize Microsoft's latest suite of enterprise AI tools and services., You'll design and build our machine learning infrastructure - starting with vector search and retrieval-augmented generation and expanding into fine-tuned LLMs with human feedback loops. You'll work across product and engineering to embed intelligent behaviors into our no-code form builder. This is not a research job or a sandbox role - it's a real opportunity to push AI into production at scale.

What you'll do

 * Build and tune vector-based retrieval pipelines using OpenAI embeddings and Azure AI Search
 * Design prompt strategies and agents to translate parsed PDF data into form component schemas
 * Fine-tune LLMs for structured output generation with low-latency performance in mind
 * Lead the development of an RLHF loop that incorporates builder UI feedback and audit data
 * Help architect systems that blend traditional APIs and probabilistic inference reliably
 * Work alongside full-stack and platform engineers to get it all running in production
 * Stay plugged into the latest model capabilities, and make smart calls on what to adopt

Tech you'll use

 * Azure AI Studio, Azure OpenAI, GPT-4o
 * Python (for agents, functions, orchestration), .NET 8 (for integration layers)
 * Azure AI Search, CosmosDB, MSSQL
 * Kubernetes (AKS), Azure Blob, Octopus for CI/CD
 * Extend.ai for structured PDF parsing, * You've shipped a working vector search + RAG pipeline integrated into our form builder
 * You've scoped and kicked off our first LLM fine-tuning cycle
 * We're collecting human feedback to improve model accuracy
 * You've helped define the roadmap for AI integrations across the platform