Senior Machine Learning Lead

Key Responsibilities:

• Lead the development and implementation of the GenAI Product platform, including its core components: StreamSync, RAGCore, DocForge etc.
• Manage and mentor ML scientists.
• Design and optimize Retrieval-Augmented Generation (RAG) systems for processing enterprise architecture documents.
• Develop and implement advanced NLP solutions for document analysis and knowledge extraction.
• Architect scalable ML pipelines for processing and analyzing Solution Architecture Documents (SADs).
• Collaborate with enterprise architects and stakeholders to understand requirements and deliver AI-powered solutions.
• Drive technical decision-making for ML infrastructure, model selection, and system architecture.
• Ensure compliance with enterprise standards and security requirements.
• Lead ML model evaluation, optimization, and deployment strategies.
• Establish best practices for ML development and documentation.

Required Qualifications:

• Master's in Computer Science or Machine Learning or related field.
• 5+ years of experience in machine learning, with at least 2 years in leadership roles (optional).
• Expertise in Natural Language Processing and Large Language Models.
• Knowledge of RAG systems and vector databases.
• Knowledge of enterprise software development and system architecture.
• Expert knowledge of Python and ML frameworks.
• Experience with cloud platforms (AWS) and MLOps practices.
• Understanding of enterprise architecture principles and documentation.

Preferred Qualifications:

• Familiarity with architecture documentation tools (e.g., Lucid).
• Background in enterprise solution architecture.
• Experience with vector databases (e.g., Azure CosmosDB).
• Experience in building GenAI systems (e.g., RAG).

Technical Skills:

• Machine Learning: Advanced NLP, LLMs, RAG systems, vector embeddings.
• Programming: Python, API development.
• Cloud & Infrastructure: AWS, Azure, containerization.
• Data Processing: Document processing pipelines, text analytics.
• Tools & Frameworks: Llama-index, LangChain, PyTorch/TensorFlow, vector databases, MLOps tool.