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# machine learning engineer

- **Company:** Jobot
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $160,000.0 - $200,000.0
- **Contract:** Temporary 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, Search Technologies, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Kubernetes, Cosmos DB, Machine Learning Operations
- **Published:** September 20, 2026
- **Apply:** https://www.dice.com/job-detail/468850d7-f7e7-45f1-bfed-928ef53f088f

## 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

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


## Related Videos

- [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning)
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- [Tips and Tricks for Working with JSON](https://www.wearedevelopers.com/videos/1229-tips-and-tricks-for-working-with-json)
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- [Instant KAI Sandboxes with vCluster: Multi-Tenant, Multi-Scheduler GPU Sharing](https://www.wearedevelopers.com/videos/100333-instant-kai-sandboxes-with-vcluster-multi-tenant-multi-scheduler-gpu-sharing)

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