AI/ML Engineer

Prophecy Technologies

• $125K — $150K *
Woodland Hills, CA 91367In-Person
Information Technology
Less than 5 years of experience
Today
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Job Overview by Ladders

Qualifications

  • 5-7 years of experience in AI/ML engineering.
  • Strong proficiency in Python programming.
  • Hands-on experience with machine learning and deep learning frameworks.
  • Familiarity with Generative AI technologies and methodologies.
  • Knowledge of Natural Language Processing (NLP) techniques.
  • Experience with MLOps practices and model deployment.
  • Background in cloud services like AWS, Azure, or GCP.

Responsibilities

  • Develop and deploy machine learning and AI models using Python.
  • Implement and utilize frameworks such as Scikit-learn, TensorFlow, and PyTorch.
  • Build Generative AI applications including LLMs and RAG.
  • Work with NLP, vector databases, and prompt engineering for model enhancement.
  • Create data pipelines and prepare datasets for effective model training.
  • Execute MLOps practices including model deployment and monitoring.
  • Collaborate with multidisciplinary teams for integrated project execution.

Benefits

  • Opportunities for professional development and training.
  • Flexible work environment, potentially with remote options.
  • Access to the latest AI and ML technologies.
  • Collaborative and supportive team culture.
  • Chance to work on cutting-edge projects in AI.
Full Job Description
We are seeking an experienced AI/ML Engineer to design, develop, and deploy scalable machine learning and AI solutions. The ideal candidate should have strong hands-on experience in Python, machine learning, deep learning, and Generative AI.

Key Skills & Responsibilities:
  • Develop and deploy Machine Learning and AI models using Python.
  • Experience with Scikit-learn, TensorFlow, PyTorch, and ML algorithms.
  • Build Generative AI, LLM, RAG, and AI-powered applications.
  • Work with NLP, embeddings, vector databases, and prompt engineering.
  • Develop data pipelines and prepare datasets for model training.
  • Implement MLOps, model deployment, monitoring, and CI/CD.
  • Work with cloud platforms such as AWS, Azure, or GCP.
  • Collaborate with data engineers, software engineers, product teams, and business stakeholders.
  • Optimize model performance, scalability, reliability, and accuracy.

Must-Have: Python, Machine Learning, Deep Learning, NLP, GenAI/LLMs, RAG, MLOps, Cloud.
* Ladders Estimates

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