Machine Learning Engineer Position

4 days ago

Pune, Maharashtra, India Onelab Ventures Full time
About Onelab Ventures

We are a software development company that partners with startups and enterprises to create successful, scalable products. Our expertise lies in AI, Large Language Models, SaaS, Fin tech, and Healthcare. Our agile, collaborative approach ensures that client visions are transformed into reliable, user-friendly products using the latest technologies and methodologies.


Job Overview

We are seeking a talented and motivated AI/ML Engineer to contribute to the development and deployment of cutting-edge artificial intelligence (AI) and machine learning (ML) solutions. The ideal candidate will have a strong background in machine learning algorithms, data processing techniques, and cloud services.


Key Responsibilities
  • Machine Learning Model Development: Design, develop, and implement machine learning models and algorithms to solve real-world problems and improve business processes.
  • Data Preprocessing: Work with large, complex datasets by cleaning, organizing, and preparing the data for training and validation of machine learning models.
  • Model Training and Tuning: Train machine learning models using frameworks like TensorFlow, PyTorch, or Scikit-learn, and fine-tune models to improve accuracy and performance.
  • Deployment and Maintenance: Deploy models into production environments using platforms such as AWS SageMaker, Azure ML, or Google AI, and monitor their performance in real-time applications.
  • AI Research: Stay updated on the latest research in machine learning and AI, and apply relevant findings to improve model accuracy, efficiency, and scalability.
  • Collaboration: Work closely with data scientists, data engineers, and software developers to integrate AI models into existing systems and workflows.
  • Model Evaluation and Optimization: Use evaluation metrics such as accuracy, precision, recall, F1-score, and ROC-AUC to assess model performance and identify opportunities for improvement.
  • Automation: Automate data workflows and model training pipelines using cloud services like AWS, Azure, or GCP, and tools such as Docker and Kubernetes.
  • Documentation: Maintain clear and concise documentation of the models, pipelines, and processes developed, ensuring they can be understood and maintained by the team.

Requirements
  • Experience: 2-5 years of experience in AI/ML development, with a focus on building, training, and deploying machine learning models in a production environment.
  • Programming Skills: Proficiency in programming languages such as Python, R, or Java. Experience with machine learning libraries like TensorFlow, PyTorch, or Scikit-learn.
  • Data Management: Strong understanding of databases, data structures, and ETL processes. Experience working with SQL, NoSQL, or other databases.
  • Cloud Experience: Hands-on experience with cloud platforms like AWS, Azure, or Google Cloud, particularly in deploying machine learning models and managing infrastructure.
  • Containerization: Knowledge of Docker and Kubernetes for containerizing machine learning applications and managing large-scale deployments.
  • Modeling: Solid understanding of supervised and unsupervised learning techniques, deep learning, natural language processing (NLP), and reinforcement learning.
  • Problem-Solving: Strong analytical skills and the ability to think critically about how to solve complex problems with machine learning solutions.
  • Communication: Excellent communication and collaboration skills to work with cross-functional teams and convey technical information to non-technical stakeholders.
Preferred Qualifications
  • Education: Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field. A Ph.D. in a relevant area is a plus.
  • Certifications: Certifications in AI/ML, such as AWS Certified Machine Learning Specialty, Microsoft Certified Azure AI Engineer, or Google Professional ML Engineer, are highly desirable.
  • Tools and Frameworks: Experience with tools like Jupyter, Git, and MLflow for model versioning and experimentation tracking.

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