> Markdown version of [/jobs/ext/1102100-genai-machine-learning-engineer](https://www.wearedevelopers.com/jobs/ext/1102100-genai-machine-learning-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # GenAI / Machine Learning Engineer - **Company:** PRIMUS Global Services, Inc - **Location:** United States - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Cluster Analysis, Python (Programming Language), Machine Learning, Tensorflow, Software Engineering, Systems Integration, Cloud Platform System, Feature Engineering, Retrieval-Augmented Generation, Large Language Models, Grafana, Prompt Engineering, Model Validation, Generative AI, Backend, Fastapi, AI Platforms, Machine Learning Operations, GPT, Automation Anywhere, Unsupervised Learning - **Published:** June 9, 2026 - **Apply:** https://www.dice.com/job-detail/e5a37f07-e8f3-4607-86a4-e8ddb796c77d ## About the Role Strong experience in Machine Learning model development, validation, and deployment. Expertise in supervised and unsupervised learning algorithms, including: * Regression * Classification * Clustering Experience with feature engineering and model evaluation techniques. Hands-on experience with Large Language Models (LLMs), including GPT and Ollama. Strong understanding of: * Temperature * Top-K Sampling * Context Length Management Experience with LangChain and LangGraph. Expertise in RAG (Retrieval-Augmented Generation) development. Strong backend development experience with FastAPI and Uvicorn. Experience developing MCP tools using FastMCP. Proficiency in Prompt Engineering and Chain-of-Thought techniques. Experience with observability tools such as LangSmith and LangFuse. Experience with vector databases and embeddings, including PGVector and Ollama Embeddings. Strong problem-solving and analytical skills. Preferred Skills Experience deploying AI/ML applications in cloud environments. Knowledge of MLOps and model lifecycle management. Experience with Python-based AI/ML ecosystems. Familiarity with enterprise-scale AI application development and deployment. ## Description We are seeking a highly skilled GenAI / Machine Learning Engineer to design, develop, and deploy AI-powered solutions leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and modern machine learning frameworks. The ideal candidate will have hands-on experience building scalable AI applications, developing ML models, and integrating Generative AI solutions into enterprise environments., * Design, develop, validate, and deploy machine learning models using supervised and unsupervised learning techniques. * Build and optimize Generative AI applications utilizing Large Language Models (LLMs) such as GPT and Ollama. * Develop Retrieval-Augmented Generation (RAG) pipelines for enterprise AI solutions. * Create and maintain AI workflows using LangChain and LangGraph frameworks. * Implement prompt engineering strategies, chain-of-thought reasoning, and model optimization techniques. * Configure and tune LLM parameters including Temperature, Top-K, and Context Length for optimal performance. * Develop and integrate MCP tools using FastMCP. * Build scalable backend APIs and AI services using FastAPI and Uvicorn. * Implement observability, monitoring, and tracing solutions using LangSmith and LangFuse. * Design and manage vector databases and embedding solutions using PGVector and Ollama Embeddings. * Collaborate with cross-functional teams to deploy AI/ML solutions into production environments. * Evaluate model performance and continuously improve accuracy, reliability, and scalability. ## Related Videos - [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock) - [Intro to FastAPI](https://www.wearedevelopers.com/videos/462-intro-to-fastapi) - [ Evaluating AI models for code comprehension](https://www.wearedevelopers.com/videos/1462-evaluating-ai-models-for-code-comprehension) - [How E.On productionizes its AI model & Implementation of Secure Generative AI.](https://www.wearedevelopers.com/videos/623-how-e-on-productionizes-its-ai-model-implementation-of-secure-generative-ai) - [OpenAPI meets OpenAI](https://www.wearedevelopers.com/videos/1150-openapi-meets-openai) - [Nest.js - TypeScript in the backend can also be clean](https://www.wearedevelopers.com/videos/1033-nest-js-typescript-in-the-backend-can-also-be-clean) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [The Best Large Language Models on The Market](https://www.wearedevelopers.com/magazine/319-the-best-large-language-models-on-the-market) - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)