AI/ML Engineer

Prophecy Technologies

$125K — $150K *
Enterprise Technology
Less than 5 years of experience
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
  • Knowledge of Generative AI, LLMs, and RAG technologies
  • Experience in MLOps and model deployment practices
  • Familiarity with cloud platforms (AWS, Azure, GCP)
  • Solid understanding of natural language processing (NLP) techniques

Responsibilities

  • Develop and deploy machine learning and AI models using Python
  • Create Generative AI applications including LLMs and RAG systems
  • Implement MLOps practices for model monitoring and deployment
  • Collaborate with cross-functional teams including data engineers and product managers
  • Build and optimize data pipelines for training datasets
  • Utilize frameworks like Scikit-learn, TensorFlow, and PyTorch
  • Enhance model performance and scalability

Benefits

  • Flexible working hours
  • Opportunities for professional development
  • Access to the latest tools and technologies
  • Collaborative work environment
  • Health and wellness programs
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.

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