

Generative AI Engineer
β - Featured Role | Apply direct with Data Freelance Hub
This role is for a Generative AI Engineer, offering a remote contract focused on AI and Python. Key skills include experience with OpenAI, RAG pipelines, and LLM applications. Familiarity with MLOps and integration of generative AI into enterprise systems is essential.
π - Country
United States
π± - Currency
$ USD
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π° - Day rate
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ποΈ - Date discovered
June 28, 2025
π - Project duration
Unknown
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ποΈ - Location type
Remote
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π - Contract type
Unknown
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π - Security clearance
Unknown
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π - Location detailed
United States
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π§ - Skills detailed
#Deployment #MongoDB #PyTorch #Transformers #Knowledge Graph #Neo4J #Pandas #Hugging Face #"ETL (Extract #Transform #Load)" #Python #Quality Assurance #Databases #Libraries #Langchain #Model Evaluation #Documentation #AI (Artificial Intelligence) #Programming #TensorFlow
Role description
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Job Title: Gen AI Engineer Developer
Job Location: Remote (EST/CST time zones)
Key Technology: AI, Python
Job Responsibilities:
As we transform our customer service desktop, the foundational components should be built in a way that Gen AI capabilities can be built / integrated as needed to
β’ Enhance Customer Experience (Personalized interactions; Faster and more efficient self-service),
β’ Improved Agent Productivity (Real-time agent assistance; Automated call summarization and documentation, simplified workflows; streamlined agent onboarding and training),
β’ Streamlined Operations (Intelligent call routing; Automated quality assurance and coaching; Predictive Analytics and insights)
Skills and Experience Required:
β’ Strong programming skills in Python, and familiarity with libraries like Transformers, Pandas, scikit-learn, Seaborn, LangChain, LlamaIndex, PyTorch, or TensorFlow.
β’ Experience building applications with OpenAI, Anthropic Claude, Google Gemini, or opensource LLMs.
β’ Working knowledge of retrieval-augmented generation (RAG) pipelines and vector databases.
β’ Understanding of MLOps, model evaluation, prompt tuning, and deployment pipelines.
β’ Build and tune LLM-based applications using platforms like Vertex, GPT, Hugging Face, etc.
β’ Create robust prompt engineering strategies and reusable prompt templates.
β’ Integrate generative AI with enterprise applications using APIs, knowledge graphs, vector databases (e.g., PG Vector, Neo4j, Mongodb), and orchestration tools.