Job Title: Senior AI/ML Engineer About Us: Nexus Ocean is revolutionizing the workforce and business productivity, starting with the maritime industry by leveraging advanced AI technologies to solve real-world challenges. We specialize in building cutting-edge AI models in a dynamic, fast-paced startup environment and are backed by technology giants Microsoft, Google and Nvidia. Our team thrives on innovation, collaboration, and a passion for delivering impactful solutions. We are seeking a highly skilled Senior AI/ML Engineer with expertise in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) architectures, classical machine learning, and a strong foundation in natural language processing (NLP). The ideal candidate will also be prepared to lead efforts in training and deploying small language models like Llama 3.2 or Mistral in the near future. Key Responsibilities: 1. Model Development and Optimization: ● Design, develop, and fine-tune Large Language Models (LLMs) for diverse maritime applications. ● Train and optimize small, open-source language models such as Llama 3.2, Mistral, and emerging architectures. ● Build embedding models tailored for the maritime domain, handling terms like vessel, cargo, and shipment. 2. Advanced RAG and Knowledge Graph Implementation: ● Implement and enhance Retrieval-Augmented Generation (RAG) pipelines for advanced information retrieval and text generation. ● Integrate RAG models with Knowledge Graph-based approaches for complex use cases, such as recommendation systems and hybrid search architectures. 3. Evaluation and Performance Metrics: ● Develop robust evaluation pipelines to assess model performance using metrics such as precision, recall, F1-score, and relevance. ● Conduct rigorous testing and validation to ensure models are reliable in production environments. 4. Research and Collaboration: ● Stay updated on the latest advancements in AI, NLP, and machine learning, with a focus on zero-shot and few-shot learning methodologies. ● Collaborate with cross-functional teams to integrate AI solutions into production systems, ensuring scalability and performance. 5. Infrastructure and Deployment: ● Develop and maintain scalable pipelines for large-scale model training and evaluation. ● Optimize and deploy models in cloud environments like Azure and Google Cloud Platform (GCP). 6. Team Leadership and Mentorship: ● Mentor junior engineers, fostering a culture of knowledge sharing and growth. ● Lead discussions on classical machine learning concepts, such as precision, recall, TF-IDF, POS tagging, and their practical applications. ● Guide the team in understanding Gen AI concepts like zero-shot learning, few-shot learning, RAG, and Knowledge Graph approaches. Key Qualifications: Technical Expertise: ● ~5 years of experience in fine-tuning large-scale AI models and implementing LLMs. ● Strong programming skills in Python, with expertise in frameworks such as PyTorch, TensorFlow, and libraries like LangChain. ● Deep understanding of classical ML concepts, including: ○ Precision, recall, and F1-score: Understanding trade-offs with real-world examples. ○ TF-IDF: Calculation, significance, and applications in search relevance. ○ POS tagging: Usage in NLP tasks and tools for efficient implementation. ● Familiarity with embedding models and their applications in domain-specific tasks. 2. Gen AI and Hybrid Architectures: ● Experience with zero-shot and few-shot learning strategies for fine-tuning LLMs. ● Strong understanding of RAG-based architectures and Knowledge Graph implementations, including how to merge the two approaches for hybrid systems. 3. Cloud Deployment and Scalability: ● Hands-on experience deploying AI models in cloud environments, specifically Azure and GCP. ● Experience in building scalable pipelines for training and inference. 4. Collaboration and Problem Solving: ● Excellent analytical skills, with the ability to design creative and scalable solutions. Soft skills: Strong team player with a collaborative mindset, thriving in high-growth, fast-paced startup environments. 5. Continuous Learning and Adaptability: Passionate about staying ahead in AI/ML developments, with the ability to integrate cutting-edge innovations into practical applications. What We Offer: A collaborative and innovative work environment where your ideas matter. Opportunities to work on impactful projects that shape the future of AI in the maritime industry.A dynamic startup culture with significant career growth opportunities. Competitive compensation and benefits. Competitive salary, Flexible and remote work, Opportunity to lead, work on cutting edge Gen AI solutions. For successful candidates, we would be open to considering equity benefits and other associated perks.This role is critical to Nexus Ocean’s success in delivering state-of-the-art AI solutions. If you are passionate about AI, thrive in challenging environments, and are ready to lead efforts in training and optimizing small language models, we invite you to join us and make an impact in a high-energy startup setting. Must-Know Topics for Senior AI/ML Engineer Classical Machine Learning (ML): 1. Precision, Recall, and F1-Score: ○ Understand trade-offs with real-world examples (e.g., maritime data retrieval scenarios). 2. TF-IDF: ○ Calculation and significance in search relevance. 3. POS Tagging: ○ Tools and usage for key term extraction and search optimization. Gen AI (Generative AI): 1. Zero-Shot vs. Few-Shot Learning: ○ Zero-Shot: No examples in the system prompt. ○ Few-Shot: Provide examples to guide the model’s responses. 2. RAG vs. Knowledge Graph Approaches: ○ RAG: Retrieval-based (dynamic answers from a database or source). ○ Knowledge Graph: Static relationships and metadata (recommendation systems). ○ Hybrid Use Cases: Combining Knowledge Graphs with RAG for advanced applications. Large Language Models (LLMs): 1. Model Fine-Tuning: ○ Techniques for small open-source models like Llama 3.2, Mistral, etc. 2. Embedding Models: ○ Custom-built for domain-specific terms (e.g., vessel, cargo, shipment). Evaluation and Deployment: 1. Model Evaluation Metrics: ○ Precision, recall, relevance, and feedback-driven improvements. 2. Cloud Deployment: ○ Experience deploying and optimizing models on Azure and Google Cloud Platform (GCP). Soft Skills and Collaboration: 1. Team Leadership: ○ Mentor junior engineers in ML, NLP, and Gen AI concepts. 2. Problem Solving: ○ Creative, scalable solutions in high-growth startup environments.
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