Machine Learning Professional

2 days ago


Jodhpur, Rajasthan, India beBeeMachine Full time ₹ 1,00,00,000 - ₹ 2,50,00,000
Machine Learning Engineer Position

We are seeking a skilled Machine Learning Engineer to join our team. The ideal candidate will have experience in designing, building, and deploying intelligent systems and machine learning models.

This role involves hands-on work on AI model development, deployment, data engineering, feature work, research, collaboration, and growth. The successful candidate will be able to stay on top of AI trends, experiment with new ideas, and bring forward practical innovations to our products.

  • Key Responsibilities:
  • Ai Model Development & Deployment: Design, train, fine-tune NLP, Generative AI, and Intelligent Networking models. Optimize deep neural networks and transformer architectures. Deploy and scale models via Docker/Kubernetes, REST/gRPC APIs.
  • Data Engineering & Feature Work: Preprocess and clean structured/unstructured data. Build robust pipelines using Airflow, Spark, or custom ETL. Implement advanced feature extraction and dimensionality reduction.
  • Research & Innovation: Stay on top of AI trends. Experiment and bring forward practical innovations to our products. Document learnings via internal knowledge bases, blogs, or POCs.
  • Collaboration & Growth: Work closely with product owners, researchers, engineers, and client-facing teams. Take ownership of technical discussions with clients when needed. Mentor junior engineers as the team scales.
  • Requirements:
  • 3–5 years of hands-on AI/ML experience (model development + deployment).
  • Proficiency in Python and modern frameworks (PyTorch, TensorFlow, JAX, VertexAI).
  • Experience with NLP, Generative AI, Reinforcement Learning.
  • Familiarity with model serving, cloud platforms (Any one cloud from AWS/GCP/Azure is fine).
  • Knowledge of vector databases, embeddings, RAG pipelines.
  • Strong problem-solving mindset, willingness to own outcomes.
  • Excellent communication and adaptability in startup environments.


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