Job Url: https://www.linkedin.com/jobs/search/?currentJobId=4341204782&f_AL=true&f_TPR=r86400&f_WT=2&keywords=machine%20learning%20engineer&origin=JOB_SEARCH_PAGE_JOB_FILTER&spellCorrectionEnabled=true Job Description: Neutrino Advisory, an Inc 5000 Company Share Show more options Machine Learning Engineer  New Jersey, United States · 22 hours ago · Over 100 applicants Promoted by hirer · Company review time is typically 1 week $63/hr Remote Matches your job preferences, workplace type is Remote. Contract Easy Apply Save Save Machine Learning Engineer  at Neutrino Advisory, an Inc 5000 Company Machine Learning Engineer Neutrino Advisory, an Inc 5000 Company · New Jersey, United States (Remote) Easy Apply Save Save Machine Learning Engineer  at Neutrino Advisory, an Inc 5000 Company Show more options Get personalized tips to stand out to hirers Practice mock interviews personalized to every role and get custom feedback Try Premium for PKR0 Meet the hiring team Adreana Jefferson, PHR, SHRM-CP 3rd Human Resources Manager at Neutrino Advisory Job poster Message About the job We are seeking a skilled and forward-looking Machine Learning Engineer with expertise in Large Language Models (LLMs), Generative AI, and Agentic Architectures to join our growing R&D and Applied AI team. This role is pivotal in helping Oversight deliver the next generation of agentic AI systems for enterprise spend management and risk controls. You will collaborate closely with AI/ML researchers, data engineers, and product teams to design, implement, and optimize intelligent systems that power autonomous exception resolution, anomaly detection, and explainable insights. This is a hands-on engineering role, where you will both build and scale ML systems and contribute to cutting-edge applied research in agentic AI. Key Responsibilities: Core ML/LLM Engineering Design, train, fine-tune, and deploy ML/LLM models for production. Implement Retrieval-Augmented Generation (RAG) pipelines using vector databases. Prototype and optimize multi-agent workflows using LangChain, LangGraph, and MCP. Develop prompt engineering, optimization, and safety techniques for agentic LLM interactions. Integrate memory, evidence packs, and explainability modules into agentic pipelines. Work with multiple LLM ecosystems, including: OpenAI GPT (GPT-4, GPT-4o, fine-tuned GPTs) Anthropic Claude (Claude 2/3 for reasoning and safety-aligned workflows) Google Gemini (multimodal reasoning, advanced RAG integration) Meta LLaMA (fine-tuned/custom models for domain-specific tasks) Data & Infrastructure Collaborate with Data Engineering to build and maintain real-time and batch data pipelines supporting ML/LLM workloads. Conduct feature engineering, preprocessing, and embedding generation for structured and unstructured data. Implement model monitoring, drift detection, and retraining pipelines. Utilize cloud ML platforms such as AWS SageMaker and Databricks ML for experimentation and scaling. Research & Applied Innovation Explore and evaluate emerging LLM/SLM architectures and agent orchestration patterns. Experiment with generative AI and multimodal models (text, images, structured financial data). Collaborate with R&D to prototype autonomous resolution agents, anomaly detection models, and reasoning engines. Translate research prototypes into production-ready components. Collaboration & Delivery Work cross-functionally with R&D, Data Science, Product, and Engineering teams to deliver AI-driven business features. Participate in architecture discussions, design reviews, and model evaluations. Document experiments, processes, and results for effective knowledge sharing. Mentor junior engineers and contribute to best practices in ML engineering. Education, Experience, and Skills Required: Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or a related field. 3+ years of experience building and deploying ML systems. Strong programming skills in Python, with experience in PyTorch, TensorFlow, Scikit-learn, and Hugging Face Transformers. Hands-on experience with LLMs/SLMs (fine-tuning, prompt design, inference optimization). Demonstrated expertise in at least two of the following: OpenAI GPT (chat, assistants, fine-tuning) Anthropic Claude (safety-first reasoning, summarization) Google Gemini (multimodal reasoning, enterprise APIs) Meta LLaMA (open-source fine-tuned models) Familiarity with vector databases, embeddings, and RAG pipelines. Proficiency in handling structured and unstructured data at scale. Working knowledge of SQL and distributed frameworks such as Spark or Ray. Strong understanding of the ML lifecycle — from data prep and training to deployment and monitoring. Preferred Qualifications: Experience with agentic frameworks such as LangChain, LangGraph, MCP, or AutoGen. Knowledge of AI safety, guardrails, and explainability. Hands-on experience deploying ML/LLM solutions in AWS, GCP, or Azure. Experience with MLOps practices — CI/CD, monitoring, and observability. Background in anomaly detection, fraud/risk modeling, or behavioral analytics. Contributions to open-source AI/ML projects or applied research publications. We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, sex, gender, gender expression, sexual orientation, age, marital status, veteran status, or disability status. We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform crucial job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation. Job search faster wit