Generative AI Engineer

Role: Generative AI Engineer with AWS

Location: 100% Remote

Contract role

Mandatory Skills : AWS Sagemaker , AWS BedRock ,Generative AI

Job Overview:

AWS Experience - AWS Sagemaker is required, AWS BedRock would be a nice to have.

Model Building, Accuracy Metrics, Finetuning - standard Data Science skillset.

Proven expertise in model finetuning for LLMs - PEFT, LORA techniques would be a big plus.

Able to understand what technique to use for data type.

RAG Experience would be great to have - similar to AI Engineer.

Machine Learning Engineering:
• Develop, train, and deploy ML models, ensuring they are optimized for production environments.
• Create and maintain automated feedback loops to enhance model accuracy and performance.
• Implement ML pipelines for continuous evaluation and refinement of models in production.

AI Orchestration & Integration:
• Integrate Large Language Models (LLMs) into business applications.
• Build AI orchestration systems to manage the end-to-end lifecycle of AI models, including deployment and scaling.
• Work with Vector Databases (VectorDB) to store and query high-dimensional data for AI applications.

Model Evaluation & Feedback Loops:
• Set up evaluation metrics and processes to assess model performance over time.
• Create feedback loops using real-world data to improve model reliability and accuracy.

Text-to-SQL & Generative AI-driven Solutions:
• Develop GenAI-driven Text-to-SQL solutions to automate database queries based on natural language input.
• Optimize GenAI workflows for database interactions and information retrieval.

Embedding/Chunking & Prompt Engineering:
• Design and implement embedding and chunking strategies for scalable data processing.
• Utilize prompt engineering techniques to fine-tune the performance of AI models in production environments.

Required Qualifications:
• Bachelor's or Master's degree in Computer Science, AI, Machine Learning, or a related field.
• Proven experience in building, deploying, and maintaining ML models in production environments.
• Proficiency in programming languages like Python, and frameworks such as TensorFlow, PyTorch, or similar.
• Familiarity with LLMs, VectorDB, embedding/chunking strategies, and AI orchestration tools.
• Strong understanding of model evaluation techniques and feedback loop systems.
• Hands-on experience with Text-to-SQL and prompt engineering methodologies.
• Knowledge of cloud platforms (AWS) and containerization tools (Docker, Kubernetes).