Senior Machine Learning Engineer

Requirements

• Design, develop, and deploy scalable machine learning models, with a focus on deep learning applications in image processing, computer vision, and NLP.
• Build and maintain end-to-end pipelines for training, evaluation, deployment, and monitoring of ML models.
• Apply expertise in OCR and computer vision to develop intelligent document understanding solutions.
• Collaborate with cross-functional teams including software engineers, product managers, and data scientists to integrate ML solutions into products.
• Drive experimentation and prototyping using frameworks such as TensorFlow, PyTorch, Keras, and Scikit learn.
• Utilize Gen AI frameworks (e.g., Langchain, Langgraph) to build and optimize conversational agents and LLM-based applications.
• Conduct code reviews, mentor junior team members, and contribute to the overall machine learning strategy and best practices.
• Stay up-to-date with the latest advancements in machine learning and AI research to incorporate state-of the-art techniques into our solutions.
  

Required Skills & Qualifications

• Experience: 5+ years of professional experience in machine learning, deep learning, or related fields.
• Programming Languages: Proficiency in Python and experience with ML libraries including TensorFlow, PyTorch, Keras, and Scikit-learn.
• Computer Vision: Strong experience in image processing, OpenCV, and vision-based model development.
• OCR: Hands-on experience developing and deploying OCR pipelines and document processing solutions.
• NLP & Gen AI: Solid background in natural language processing and experience with generative AI technologies, including Langchain and Langgraph.
• ML Engineering: Experience building robust ML pipelines and deploying models into production environments.
• Soft Skills: Excellent problem-solving skills, strong communication abilities, and a collaborative mindset.
  

Preferred Qualifications

• Master's or Bachelor’s in Computer Science, Machine Learning, AI, or a related field.
• Experience with cloud platforms (AWS, GCP, or Azure) and ML infrastructure tools (e.g., MLflow, Docker, Kubernetes).
  

Benefits