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AI / Machine Learning Engineer

Roles & Responsibilities

  • 4+ years in machine learning engineering or applied ML roles
  • Strong Python and ML libraries (scikit-learn, PyTorch or TensorFlow)
  • Experience deploying models to cloud (AWS, Azure, or GCP)
  • Familiarity with LLMs, embeddings, and vector search for GenAI use cases

Requirements:

  • Build and improve ML models and LLM-powered features (RAG, agents, or classification/NLP as needed)
  • Prepare training and evaluation datasets; implement reproducible experiments
  • Integrate models into applications via APIs, batch jobs, or real-time services
  • Implement monitoring for drift, latency, cost, and quality; iterate from feedback

Job description

Title: AI / ML Engineer — Remote (US)

Location: Remote — United States
Employment type: Contract (6–12+ months)
Indicative rate: $85–$115/hr

Role Summary
We are seeking an AI / machine learning engineer to develop and deploy ML and GenAI capabilities into production applications. Work spans experimentation, model integration, evaluation, and MLOps on a remote US team.

Responsibilities

  • Build and improve ML models and LLM-powered features (RAG, agents, or classification/NLP as needed)
  • Prepare training and evaluation datasets; implement reproducible experiments
  • Integrate models into applications via APIs, batch jobs, or real-time services
  • Implement monitoring for drift, latency, cost, and quality; iterate from feedback
  • Collaborate with product, data engineering, and security on responsible AI practices
  • Document architectures, prompts, evaluation metrics, and runbooks
  • Support CI/CD and containerized deployment of ML services

Required Qualifications

  • 4+ years in machine learning engineering or applied ML roles
  • Strong Python and ML libraries (scikit-learn, PyTorch or TensorFlow)
  • Experience deploying models to cloud (AWS, Azure, or GCP)
  • Familiarity with LLMs, embeddings, and vector search for GenAI use cases
  • Solid software engineering practices: Git, testing, code review
  • Remote in the United States

Preferred Qualifications

  • LangChain, LlamaIndex, or similar orchestration frameworks
  • MLOps: MLflow, SageMaker, Azure ML, Vertex AI, or Kubeflow
  • RAG design: chunking, retrieval, evaluation (RAGAS or custom benchmarks)
  • Experience with guardrails, PII redaction, and enterprise security reviews

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