Full-Stack GenAI Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Full-Stack GenAI Engineer, onsite, with a contract length of "unknown" and a pay rate of "unknown." Key skills include Python, LangChain, AWS, and Azure. A Master's degree and 3+ years in AI development are preferred.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
-
🗓️ - Date discovered
September 18, 2025
🕒 - Project duration
Unknown
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🏝️ - Location type
On-site
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📄 - Contract type
Unknown
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🔒 - Security clearance
Unknown
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📍 - Location detailed
Iselin, NJ
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🧠 - Skills detailed
#FastAPI #Automated Testing #Monitoring #React #Databases #AI (Artificial Intelligence) #PostgreSQL #Data Processing #Langchain #Azure DevOps #Scala #"ETL (Extract #Transform #Load)" #Spark (Apache Spark) #DevOps #PySpark #TypeScript #Streamlit #AWS (Amazon Web Services) #ADF (Azure Data Factory) #Redshift #Terraform #.Net #OpenCV (Open Source Computer Vision Library) #Python #Azure #SageMaker #ML (Machine Learning) #C++ #Cloud #Programming #MongoDB #Django #Microsoft Azure #AWS SageMaker #Azure Data Factory #PyTorch #Airflow #AWS Glue #Computer Science
Role description
Job Title: Full-Stack GenAI Engineer
Location: Onsite
Overview:
We are seeking a highly skilled and innovative Full-Stack GenAI Engineer to join our client’s AI team. The ideal candidate will have hands-on experience building end-to-end AI solutions, including LLM-based applications, retrieval-augmented generation (RAG) systems, and scalable infrastructure. This role requires a blend of software engineering, machine learning, and prompt engineering expertise to deliver impactful AI products.
Key Responsibilities:
• Design and deploy agentic LLM systems using LangChain, FastAPI, and front-end frameworks.
• Optimize model performance through quantization (e.g., 4-bit LLaMA models) and latency improvements.
• Build and maintain RAG pipelines using hybrid sparse-dense retrieval, semantic compression, and chunk-chaining.
• Fine-tune prompts and client queries using DSPy, adapters, and prompt engineering techniques.
• Develop and deploy ML models using tools like Kubeflow, Airflow, and Azure/AWS infrastructure.
• Create and manage ETL pipelines for structured and unstructured data using MongoDB, PostgreSQL, AWS Glue, and Azure Data Factory.
• Mentor junior engineers and contribute to open-source projects and internal research initiatives.
• Integrate telemetry and automated testing for robust AI system monitoring and validation.
Required Skills & Technologies:
• Programming & Frameworks: Python, PySpark, .NET, Django, FastAPI, NextJS, TypeScript, React
• AI/ML Tools: LangChain, PyTorch, Huggingface, OpenCV, DSPy, Whisper.cpp, Granite adapters
• Cloud & DevOps: AWS (Sagemaker, Glue, Redshift), Azure (OpenAI, Bot Framework, LUIS), Terraform, CloudFormation, Azure DevOps
• Databases: PostgreSQL, MongoDB, Pinecone
• Certifications: Microsoft Azure Fundamentals (AZ-900) preferred
• Other Tools: Kubeflow, Airflow, Ngrok, Streamlit, OCR (pytesseract), VLM APIs
Preferred Qualifications:
• Master’s degree in Computer Science or related field
• 3+ years of professional experience in software engineering or AI development
• Experience with multimodal data processing and retrieval systems
• Strong communication skills and ability to engage with clients and stakeholders
• Demonstrated success in hackathons, open-source contributions, or research projects
Job Title: Full-Stack GenAI Engineer
Location: Onsite
Overview:
We are seeking a highly skilled and innovative Full-Stack GenAI Engineer to join our client’s AI team. The ideal candidate will have hands-on experience building end-to-end AI solutions, including LLM-based applications, retrieval-augmented generation (RAG) systems, and scalable infrastructure. This role requires a blend of software engineering, machine learning, and prompt engineering expertise to deliver impactful AI products.
Key Responsibilities:
• Design and deploy agentic LLM systems using LangChain, FastAPI, and front-end frameworks.
• Optimize model performance through quantization (e.g., 4-bit LLaMA models) and latency improvements.
• Build and maintain RAG pipelines using hybrid sparse-dense retrieval, semantic compression, and chunk-chaining.
• Fine-tune prompts and client queries using DSPy, adapters, and prompt engineering techniques.
• Develop and deploy ML models using tools like Kubeflow, Airflow, and Azure/AWS infrastructure.
• Create and manage ETL pipelines for structured and unstructured data using MongoDB, PostgreSQL, AWS Glue, and Azure Data Factory.
• Mentor junior engineers and contribute to open-source projects and internal research initiatives.
• Integrate telemetry and automated testing for robust AI system monitoring and validation.
Required Skills & Technologies:
• Programming & Frameworks: Python, PySpark, .NET, Django, FastAPI, NextJS, TypeScript, React
• AI/ML Tools: LangChain, PyTorch, Huggingface, OpenCV, DSPy, Whisper.cpp, Granite adapters
• Cloud & DevOps: AWS (Sagemaker, Glue, Redshift), Azure (OpenAI, Bot Framework, LUIS), Terraform, CloudFormation, Azure DevOps
• Databases: PostgreSQL, MongoDB, Pinecone
• Certifications: Microsoft Azure Fundamentals (AZ-900) preferred
• Other Tools: Kubeflow, Airflow, Ngrok, Streamlit, OCR (pytesseract), VLM APIs
Preferred Qualifications:
• Master’s degree in Computer Science or related field
• 3+ years of professional experience in software engineering or AI development
• Experience with multimodal data processing and retrieval systems
• Strong communication skills and ability to engage with clients and stakeholders
• Demonstrated success in hackathons, open-source contributions, or research projects
