Remote AI Engineer

About the Role

Key Responsibilities

• Design, build, and deploy production-grade AI systems powered by large language models (LLMs) including GPT, Claude, Gemini, and open-source models like Llama and Mistral
• Develop Retrieval-Augmented Generation (RAG) pipelines using vector databases such as Pinecone, Weaviate, Chroma, or pgvector
• Build and orchestrate agentic AI workflows using frameworks like LangChain, LlamaIndex, LangGraph, or CrewAI
• Fine-tune, evaluate, and optimize foundation models using techniques including LoRA, QLoRA, RLHF, and supervised fine-tuning
• Develop robust evaluation frameworks and AI observability systems to measure model quality, hallucination rates, and production performance
• Design and implement prompt engineering strategies, including few-shot prompting, chain-of-thought reasoning, and structured output generation
• Integrate AI capabilities into existing products through APIs, SDKs, and microservices
• Collaborate with product, data science, and engineering teams to translate business requirements into scalable AI solutions
• Deploy and monitor AI workloads on AWS SageMaker, Azure ML, Google Vertex AI, or Hugging Face Inference Endpoints
• Implement MLOps best practices including model versioning, CI/CD for ML, A/B testing, and automated retraining pipelines
• Stay current with emerging AI research, open-source models, and industry best practices in generative and agentic AI

Requirements

• 3+ years of experience as an AI engineer, machine learning engineer, or applied ML researcher
• Strong proficiency in Python and modern ML libraries including PyTorch, TensorFlow, and Hugging Face Transformers
• Hands-on experience building production applications with LLMs via OpenAI, Anthropic, Google, or open-source model APIs
• Demonstrated experience building RAG systems, including document chunking, embeddings, vector search, and reranking
• Working knowledge of at least one agent framework (LangChain, LlamaIndex, LangGraph, AutoGen, or CrewAI)
• Experience with vector databases and semantic search implementations
• Familiarity with cloud AI platforms — AWS, Microsoft Azure, or Google Cloud Platform
• Understanding of MLOps tooling such as MLflow, Weights & Biases, Kubeflow, or SageMaker Pipelines
• Experience with containerization (Docker) and API frameworks (FastAPI, Flask)
• Strong grasp of software engineering fundamentals — version control, testing, code review, system design
• Relevant credentials are a plus: published ML research, open-source contributions, AWS/Azure/GCP ML certifications, or a portfolio of shipped AI projects
• Ability to work independently in a fully remote environment and communicate technical concepts to non-technical stakeholders

Benefits & Perks

• Competitive salary with bonus and equity opportunity
• Medical, dental, and vision insurance
• Flexible PTO and paid holidays
• Fully remote with flexible scheduling
• 401(k) with employer match
• Annual learning & development budget including GPU credits, conference attendance, and certifications
• Access to premium AI tools and API credits (OpenAI, Anthropic, and more)
• Home office stipend and top-tier equipment provided
• Team offsites and virtual collaboration culture
• Clear career path into Senior AI Engineer, Staff AI Engineer, AI Tech Lead, or Head of AI roles