AL/ML Engineer

Job highlights

• Bachelor s or Master s degree in Computer Science,Data Machine Learning,or a related field. . 3-4 years of hands-on experience in machine learning,NLP,OCR,and production environments. . Proficiency in Python and ML frameworks like TensorFlow,PyTorch,or Keras. . Strong experience in API development (FastAPI,Flask,or Django).

The role of a Machine Learning (ML) Engineer is centered around the application of machine learning algorithms and statistical models to solve real-world problems. ML engineers build and deploy models that allow systems to improve automatically through experience and data, often contributing to the development of AI (Artificial Intelligence) products. What you ll do

• Design, Build, and Optimize Machine Learning Models.
• Develop predictive or classification models.
• Optimize models for scalability and performance.
• Deploy models into production.
• Collaborate with the sales team to understand client needs.
• Present technical solutions,demonstrate how they meet client objectives.
• Participate in client meetings and contribute to proposal development.
• Work with various departments, including Engineering and sales.
• Communicate complex concepts to technical stakeholders.
• Monitor and maintain live models.
• Stay current with industry trends, tools, and technologies.
• Participate in professional development opportunities.

What you ll need

• Bachelor s or Master s degree in Computer Science, Data Machine Learning, or a related field.
• 3-4 years of hands-on experience in machine learning, NLP, OCR, and production environments.
• Proficiency in Python and ML frameworks like TensorFlow, PyTorch, or Keras.
• Strong experience in API development (FastAPI, Flask, or Django)
• Knowledge of queuing systems like RabbitMQ, Redis, or Celery for handling a synchronous tasks.
• Expertise in NLP techniques and OCR tools (Tesseract, OpenCV). Experience with NLP libraries (SpaCy, NLTK) and OCR frameworks.
• Familiarity with cloud platforms (AWS, Google Cloud) and MLOps practices for streamlined model deployment.