Machine Learning Engineer

4 weeks ago

Mohali, India Paxcom Full time

Job Summary:

We are seeking a highly skilled and experienced AI/ML Engineer to join our growing AI team. The ideal candidate will have a strong foundation in machine learning, deep learning, and data science, with hands-on experience in building scalable AI solutions using open-source tools and cloud infrastructure. You will work on cutting-edge projects involving generative AI, predictive modeling, and intelligent automation.

Key Responsibilities:

  • Design, build, and deploy advanced ML models for applications such as forecasting, anomaly detection, clustering, trend analysis, and pattern recognition.
  • Develop and optimize GenAI solutions leveraging models like GPT-3.5/4/5, LLaMA 2, Falcon, Gemini and apply prompt engineering best practices.
  • Build and maintain basic Retrieval-Augmented Generation (RAG) pipelines.
  • Process and analyze both structured and unstructured data from diverse sources.
  • Implement, test, and deploy ML models using FastAPI, Flask, Docker, and similar frameworks.
  • Conduct data preprocessing, feature engineering, and statistical analysis to prepare datasets for modeling.
  • Collaborate with cross-functional teams to integrate models into production systems hosted on AWS (EC2, S3, ECR).
  • Evaluate model performance using standard metrics and iterate on improvements.

Required Skills and Experience:

  • 4+ years of hands-on experience in AI/ML and Data Science with a strong grasp of open-source ML/DL tools.
  • Proficient in Python and data science libraries such as NumPy, SciPy, Scikit-learn, Matplotlib, and CUDA for GPU computing.
  • Strong experience in at least one of the following:
  • Time Series Analysis
  • Standard Machine Learning Algorithms
  • Deep Learning Architectures
  • Hands-on experience with GenAI models and prompt engineering techniques.
  • Working knowledge of cloud platforms, preferably AWS.
  • Familiarity with containerization and model deployment (Docker, FastAPI, Flask).
  • Solid understanding of statistics, model validation techniques, and evaluation metrics.

Preferred Expertise (One or More):

  • Proficiency with object detection frameworks such as YOLO, Detectron, TFOD, ideally in a distributed computing environment.
  • Deep knowledge of deep learning architectures like CNN, RNN, Transformers (LSTM, ResNet, etc.).
  • Experience with NLP models and frameworks, including BERT, ELMo, GPT-2, XLNet, T5, CRFs, and ONNX.

Additional Qualities:

  • Strong analytical and problem-solving skills.
  • Ability to communicate technical concepts effectively.
  • Proactive decision-making and a strong sense of ownership.

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