

GenAI Developer
β - Featured Role | Apply direct with Data Freelance Hub
This role is for a GenAI Developer with a contract length of "unknown," offering a pay rate of "unknown." Key skills include Python, .NET, and LLM experience. Candidates should have 5+ years in software engineering and familiarity with cloud-based AI environments.
π - Country
United States
π± - Currency
$ USD
-
π° - Day rate
520
-
ποΈ - Date discovered
August 28, 2025
π - Project duration
Unknown
-
ποΈ - Location type
Unknown
-
π - Contract type
Unknown
-
π - Security clearance
Unknown
-
π - Location detailed
Plano, TX
-
π§ - Skills detailed
#Data Science #.Net #Azure #Agile #ML (Machine Learning) #AI (Artificial Intelligence) #Cloud #Deployment #Python #C#
Role description
Key Responsibilities
β’ Develop, integrate, and support GenAI-powered solutions using Python and .NET frameworks.
β’ Collaborate with engineering teams to address real-world challenges using LLMs (as consumers, not model creators).
β’ Apply deep problem-solving skills to engineer solutions β not just propose architectures.
β’ Scale AI models for enterprise-level performance, considering optimization, tokenization, and cost efficiency.
β’ Justify and implement tokenizer strategies and other AI engineering techniques for better model utility.
β’ Work cross-functionally with product owners, data scientists, and cloud engineers to deliver impactful GenAI solutions.
β’ Remain current with industry best practices and GenAI trends, adapting solutions accordingly.
Required Skills & Qualifications
β’ 5+ years of software engineering experience with a strong focus on Python development.
β’ Solid working knowledge of .NET framework (C# or ASP.NET).
β’ Demonstrated experience in delivering solution-oriented AI implementations (not just model development).
β’ Hands-on experience with LLMs (e.g., OpenAI, Azure OpenAI, HuggingFace) in real-world applications.
β’ Strong understanding of tokenization, inference strategies, and model scaling principles.
β’ Ability to explain and justify engineering decisions to both technical and non-technical stakeholders.
β’ Comfortable working in agile, cross-functional environments with high accountability.
Nice To Have
β’ Familiarity with cloud-based AI/ML environments (Azure preferred).
β’ Exposure to MLOps practices and deployment pipelines.
β’ Experience with prompt engineering, embedding models, or retrieval-augmented generation (RAG)
Skills: genai,cloud-based ai/ml environments,mlops practices,solution-oriented ai implementations,accountability,python,agile methodologies,tokenization,ai engineering,llms,azure,cloud,.net
Key Responsibilities
β’ Develop, integrate, and support GenAI-powered solutions using Python and .NET frameworks.
β’ Collaborate with engineering teams to address real-world challenges using LLMs (as consumers, not model creators).
β’ Apply deep problem-solving skills to engineer solutions β not just propose architectures.
β’ Scale AI models for enterprise-level performance, considering optimization, tokenization, and cost efficiency.
β’ Justify and implement tokenizer strategies and other AI engineering techniques for better model utility.
β’ Work cross-functionally with product owners, data scientists, and cloud engineers to deliver impactful GenAI solutions.
β’ Remain current with industry best practices and GenAI trends, adapting solutions accordingly.
Required Skills & Qualifications
β’ 5+ years of software engineering experience with a strong focus on Python development.
β’ Solid working knowledge of .NET framework (C# or ASP.NET).
β’ Demonstrated experience in delivering solution-oriented AI implementations (not just model development).
β’ Hands-on experience with LLMs (e.g., OpenAI, Azure OpenAI, HuggingFace) in real-world applications.
β’ Strong understanding of tokenization, inference strategies, and model scaling principles.
β’ Ability to explain and justify engineering decisions to both technical and non-technical stakeholders.
β’ Comfortable working in agile, cross-functional environments with high accountability.
Nice To Have
β’ Familiarity with cloud-based AI/ML environments (Azure preferred).
β’ Exposure to MLOps practices and deployment pipelines.
β’ Experience with prompt engineering, embedding models, or retrieval-augmented generation (RAG)
Skills: genai,cloud-based ai/ml environments,mlops practices,solution-oriented ai implementations,accountability,python,agile methodologies,tokenization,ai engineering,llms,azure,cloud,.net