Staff Machine Learning Engineer

PhD in Computer Science, Stats, Economics, or relevant technical field with at least 3+ years of relevant experience or MS with at least 5+ years of experience
6+ years of experience building and deploying Deep Learning models including Reinforcement algorithms, Recommendation systems, etc. with solid understanding of the mathematics, advanced statistics and engineering behind building such infra
Hands-on experience with building, fine-tuning and deploying multi-modal LLM Models and managing the end-to-end model lifecycle
Experience partnering with the engineering, product, BizOps and other data teams while designing, building and executing solutions
Deep understanding in at least three of the following areas: data mining, advanced statistics, machine learning, NLP or computer vision
Strong problem solving with bias for action

Architect and implement enterprise-grade LLM-powered solutions, managing the full lifecycle from business requirements to production deployment, monitoring, and continuous optimization
Design and develop multi-agent GenAI systems using state-of-the-art frameworks (LangChain, LlamaIndex) to orchestrate complex workflows across retrieval augmentation, data operations, and compliance verification
Engineer robust Retrieval Augmented Generation (RAG) pipelines incorporating advanced techniques such as hybrid retrieval, reranking, query expansion, and contextual compression
Implement parameter-efficient fine-tuning strategies (LoRA, QLoRA, PEFT) to adapt foundation models to domain-specific use cases while optimizing for inference costs and latency
Develop intelligent routing and orchestration systems to manage conversation state across multiple specialized AI agents, ensuring seamless transitions between different system capabilities
Build evaluation frameworks to measure and improve LLM performance across diverse metrics including factuality, coherence, task completion, and alignment with business objectives
Integrate LLM solutions with existing enterprise architecture, ensuring compliance with data security policies, authentication mechanisms, and transaction safety requirements

Demonstrable experience in parameter-efficient fine-tuning, model quantization, and quantization-aware fine-tuning of LLM models
Hands-on knowledge of Chain-of-Thoughts, Tree-of-Thoughts, Graph-of-Thoughts prompting strategies
Experience in designing and implementing pipelines using DAGs (e.g., Kubeflow, DVC, Ray)
Ability to construct batch and streaming microservices exposed as gRPC and/or GraphQL endpoints