[Remote] ML LLM Engineer with OpenAI GPT RAG and Google Gemini

What you'll work on

• 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).
• 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.
• 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.
• 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.

Skills

• 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.
• 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.

Education Requirements

• Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or a related field.

Company Overview

• Amaze Systems is a web and digital marketing agency that offers data analytics and SEO services. It was founded in 2020, and is headquartered in Dallas, Texas, USA, with a workforce of 501-1000 employees. Its website is https://www.amaze-systems.com.