Advanced AI Model Developer

19 hours ago

Mohali, Punjab, India beBeeMachineLearning Full time ₹ 20,00,000 - ₹ 25,00,000
Machine Learning Engineer

We are seeking a highly skilled Machine Learning Engineer to join our team. The ideal candidate will have a strong background in machine learning, deep learning, and MLOps.

Key Responsibilities :

  • Design, develop, and deploy advanced machine learning models to solve complex business problems.
  • Lead projects involving natural language processing (NLP), including fine-tuning transformer-based models for tasks such as classification, summarization, and generation.
  • Manage the entire ML lifecycle including data preparation, model training, evaluation, optimization, deployment, and monitoring.
  • Implement MLOps practices using tools like MLflow or Kubeflow to enable reproducibility, scalability, and automation of ML pipelines.
  • Collaborate with cross-functional teams including data engineers, product managers, and software developers to productionize models.
  • Utilize cloud platforms (AWS, GCP, Azure) and ML services (e.g., SageMaker, Vertex AI) for scalable model training and deployment.
  • Apply best practices for model optimization techniques such as quantization and pruning to enhance performance.
  • Integrate ML solutions into containerized environments using Docker, Kubernetes, and CI/CD pipelines.
  • Work with distributed computing frameworks (e.g., Apache Spark, Ray) for handling large datasets efficiently.
  • Explore and implement vector databases like FAISS or Milvus for similarity search and retrieval-based AI systems.
  • Continuously monitor model performance, implement drift detection, and conduct hyperparameter tuning for model stability and accuracy.

Required Skill Set :

  • Strong expertise in Machine Learning, Deep Learning, and MLOps
  • Proficiency in : Python, TensorFlow, PyTorch, Scikit-learn
  • Hugging Face and other NLP model libraries
  • In-depth knowledge and hands-on experience in : NLP and fine-tuning transformer models
  • Model lifecycle management and ML system design
  • Cloud platforms : AWS (SageMaker), GCP (Vertex AI), Azure ML
  • Containerization using Docker and Kubernetes
  • CID pipelines for ML workflows
  • Distributed computing frameworks : Spark, Ray
  • Vector databases : FAISS, Milvus
  • Model optimization : quantization, pruning
  • Model evaluation, hyperparameter tuning, and drift detection

Preferred Qualifications :

  • Masters or Ph.D. in Computer Science, Data Science, AI, or a related field.
  • Published work in machine learning/NLP or contributions to open-source projects.
  • Experience mentoring junior data scientists or leading project teams.

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