Principal, Software Engineer - AI

About the Role

Key Responsibilities

• Develop and deploy machine learning models using decision trees, random forests, and gradient boosting techniques (e.g., XGBoost), with a focus on scalability, interpretability, and hyperparameter optimization.
• Apply unsupervised learning techniques such as topic modeling (LDA, BERTopic) and clustering (K-means, HDBSCAN) to extract insights from unstructured data.
• Build and refine Retrieval-Augmented Generation (RAG), Conversational-Aware Generation (CAG), and Knowledge-Augmented Generation (KAG) systems, utilizing advanced chunking, hybrid retrieval, and reranking strategies.
• Design and orchestrate multi-agent LLM systems using paradigms like ReAct, self-reflection, and plan & execute, integrating tool usage and agent-to-agent communication protocols.
• Fine-tune and optimize large language models using methods like LoRA, QLoRA, quantization, prompt tuning, and distillation.
• Develop automated and human-in-the-loop evaluation frameworks for NLP tasks such as summarization, question answering, and code generation.
• Work across cloud-native AI platforms (Azure, AWS, GCP) to deploy scalable ML/AI solutions, using modern MLOps tools to streamline experimentation and production workflows.

Requirements

• Deep expertise in traditional ML techniques and frameworks, with a strong foundation in model interpretability, tuning, and real-world deployment.
• Proficiency in unsupervised learning methods for organizing and deriving value from unstructured datasets.
• Demonstrated experience with generative AI and retrieval-augmented architectures, including knowledge of advanced search and reranking techniques.
• Solid understanding of agent-based LLM frameworks and protocols (LangChain, LangGraph, AutoGen, MCP, Agent Protocol), enabling you to build sophisticated multi-agent systems.
• Hands-on experience fine-tuning LLMs using cutting-edge methods for improved efficiency and performance.
• A strategic approach to evaluation, with a track record of building robust pipelines for assessing model performance both automatically and with human feedback.
• Technical fluency across Python, PyTorch, TensorFlow, Hugging Face, and popular vector databases (Milvus, Pinecone, Qdrant), combined with experience on major cloud AI platforms and MLOps tools like MLflow, Docker, and Kubernetes.

Nice to Have

• Background in creating inclusive digital experiences, demonstrating knowledge in implementing Web Content Accessibility Guidelines (WCAG) 2.2 AA standards, assistive technologies, and integrating digital accessibility.
• Knowledge of accessibility best practices and supporting an inclusive culture.

Qualifications

• Option 1: Bachelor's degree in computer science, computer engineering, computer information systems, software engineering, or related area and 5 years' experience in software engineering or related area.
• Option 2: 7 years' experience in software engineering or related area.
• Preferred: Master's degree in computer science, computer engineering, computer information systems, software engineering, or related area and 3 years' experience in software engineering or related area.

Benefits & Perks

• Incentive awards for performance.
• 401(k) match, stock purchase plan.
• Paid maternity and parental leave, PTO, multiple health plans.
• Health benefits including medical, vision, and dental coverage.
• Paid time off benefits including sick leave, parental leave, family care leave, bereavement, jury duty, and voting.
• Short-term and long-term disability, company discounts, Military Leave Pay, adoption and surrogacy expense reimbursement.
• Live Better U education benefit program covering tuition, books, and fees.