Machine Learning Engineer

Position Overview

CareerVest is looking for a skilled Machine Learning Engineer to enhance our technology division. The successful candidate will be instrumental in creating, deploying, and maintaining scalable machine learning solutions that address intricate business problems and generate tangible value.

Key Responsibilities

• Design and implement scalable, production-ready machine learning models including predictive analytics, recommendation systems, classification, regression, clustering, and forecasting.
• Develop comprehensive ML workflows, covering data ingestion, preprocessing, feature creation, training, validation, deployment, and ongoing monitoring.
• Enhance algorithm efficiency considering performance, scalability, and accuracy.
• Construct deep learning models with frameworks like TensorFlow and PyTorch.
• Create Natural Language Processing (NLP) and Generative AI applications leveraging Large Language Models (LLMs).
• Develop Retrieval-Augmented Generation pipelines tailored for enterprise AI solutions.
• Utilize containerization and orchestration tools such as Docker and Kubernetes alongside MLflow and cloud services for deployment.
• Work collaboratively with Data Scientists, Software Engineers, Product Managers, and DevOps to translate business needs into AI-driven solutions.
• Conduct model assessments including hyperparameter tuning, A/B testing, and continuous performance tracking.
• Develop APIs and microservices for serving ML models.
• Maintain technical documentation, adhere to coding best practices, and version control protocols.
• Stay updated on the latest AI advancements and propose innovative technologies.

Qualifications and Skills

• Bachelor's or Master's degree in Computer Science, AI, Data Science, Software Engineering, Mathematics, Statistics, or a closely related area.
• Minimum of 3 years' practical experience in developing and deploying machine learning models.
• Proficient programming skills in Python; familiarity with SQL; Java and Scala are advantageous.
• Experience with machine learning frameworks such as Scikit-learn, TensorFlow, PyTorch, XGBoost, LightGBM, and CatBoost.
• Strong foundation in diverse learning techniques including supervised, unsupervised, reinforcement, and deep learning.
• Robust understanding of probability, statistics, and linear algebra principles.
• Experience handling both large structured and unstructured datasets effectively.
• Excellent analytical mindset and problem-solving capabilities.
• Strong interpersonal and communication skills for effective collaboration.