ML Engineering Lead: GenAI & Production Pipelines

Compunnel Inc.; Woodland (CA); In loco; USD 140.000 - 180.000; Tempo pieno

Descrizione del lavoro

A technology solutions company in California is looking for a Lead II - ML Engineering Data Science Engineer. In this role, you will design and develop scalable machine learning and Generative AI solutions, build data pipelines, and integrate AI frameworks. Candidates should possess expert Python skills and experience with document processing workflows, vector databases, and ML/GenAI technologies. This position offers the opportunity to work on impactful AI solutions in a dynamic environment.

Mansioni

• Develop and deploy machine learning and Generative AI solutions using Python.
• Design and optimize prompt engineering strategies.
• Build document extraction and chunking pipelines.
• Train, evaluate, and fine-tune machine learning models.
• Manage tagging and labeling workflows for model development.
• Implement embedding generation and vector search solutions.
• Integrate ML models with vector databases and MongoDB.
• Ensure code quality and production readiness.

Conoscenze

Expert-level proficiency in Python

Strong experience in machine learning training

Hands-on experience with document extraction

Solid understanding of ML algorithms and GenAI

Experience with vector databases

Strong problem-solving skills

Ability to build scalable solutions

Descrizione del lavoro

We are seeking a Lead II - ML Engineering Data Science Engineer to design and develop scalable machine learning and Generative AI solutions. This role focuses on building robust data pipelines, training and fine-tuning models, and integrating modern AI frameworks into production systems. The ideal candidate will have strong expertise in Python, experience with document processing workflows, and knowledge of vector databases and ML/GenAI technologies.

Key Responsibilities

• Develop and deploy machine learning and Generative AI solutions using Python
• Design and optimize prompt engineering strategies for large language model applications
• Build document extraction, parsing, and chunking pipelines for structured and unstructured data
• Train, evaluate, and fine-tune machine learning models
• Manage tagging and labeling workflows for model development
• Implement embedding generation and vector search solutions
• Integrate machine learning models with vector databases and MongoDB
• Ensure code quality, scalability, and production readiness of solutions

Required Qualifications

• Expert-level proficiency in Python
• Strong experience in machine learning model training, evaluation, and tagging workflows
• Hands-on experience with document extraction and data chunking techniques
• Solid understanding of machine learning algorithms and Generative AI concepts
• Experience working with vector databases and/or MongoDB
• Strong problem-solving and analytical skills
• Ability to build scalable and production-ready solutions