Machine Learning Engineer

Machine Learning Engineer

Requirements

• 3+ years of experience in machine learning, data science, or similar technical role
• Strong software engineering fundamentals and proficiency in Python
• Demonstrated expertise in: Classical ML techniques (regression, classification, clustering), Deep learning frameworks (PyTorch, TensorFlow), Natural language processing and information extraction, Large language models and prompt engineering, RAG system design and implementation, Advanced RAG techniques (multi-hop reasoning, self-consistency), Vector databases and embedding techniques, Model evaluation and validation methods
• Experience with: LLM fine-tuning (LoRA, QLoRA, instruction tuning), Knowledge graph construction and reasoning, Production ML system deployment, Distributed computing and data processing
• Excellent communication skills with ability to: Explain complex technical concepts clearly, Collaborate effectively with cross-functional teams, Write clear technical documentation
• Sound judgment in balancing technical tradeoffs with business needs

Responsibilities

• Pioneer cutting-edge Document AI systems at the forefront of generative AI innovation, building next-generation models that go beyond traditional document processing to achieve human-level understanding of complex legal and medical documents, intelligently extract key entities and relationships, perform sophisticated multi-document reasoning, and generate contextually-aware documents that transform business workflows.
• Implement and advance technologies in: Information Extraction (using traditional ML, LLMs, and multi-modal LLMs for entity recognition, relationship extraction, and document structure understanding), Information Retrieval (query understanding, semantic search, hybrid retrieval architectures, and learning-to-rank models), Data Management (schema design, knowledge graphs, distributed data pipelines, and petabyte-scale processing), RAG (Retrieval-Augmented Generation) with advanced techniques like multi-hop reasoning, chain-of-thought prompting, and self-consistency checks, Prompt Engineering (few-shot learning, instruction tuning, and context window optimization), LLM fine-tuning (parameter-efficient techniques like LoRA/QLoRA, instruction fine-tuning, and domain adaptation)
• Collaborate with domain experts (legal, healthcare, etc), product managers, and engineers to translate insights into robust machine learning systems.
• Create tools that empower internal teams and clients to make data-driven decisions.
• Mentor junior team members, promoting a culture of excellence and collaboration.

Preferred Qualifications

• Strong foundation in machine learning, information retrieval, or data management, with particular interest in LLM and generative AI technologies