This role is for one of the Weekday's clients

We are seeking an experienced AI/Machine Learning Engineer with 10+ years of expertise in machine learning and software engineering. The ideal candidate will have strong programming skills, a deep understanding of ML concepts and algorithms, and experience building scalable data-driven solutions.

Requirements

Key Responsibilities:

• Design, develop, and deploy machine learning models for a variety of applications, including supervised and unsupervised learning, deep learning, and NLP.
• Work with popular ML libraries such as scikit-learn, TensorFlow, PyTorch, Keras, NumPy, and Pandas.
• Develop and optimize scalable backend systems for data processing and analytics.
• Leverage Python, Java, or Scala to build robust and efficient ML solutions.
• Utilize cloud platforms (AWS, Azure, or Google Cloud) to deploy and scale ML models.
• Implement big data technologies such as Apache Spark, Hadoop, and Kafka to handle large-scale data pipelines.
• Apply MLOps practices using tools like MLflow, Kubeflow, or TFX to streamline the ML lifecycle.
• Use containerization (Docker) and orchestration (Kubernetes) for efficient model deployment and scaling.
• Collaborate cross-functionally to drive end-to-end machine learning projects from research to production.
• Stay updated with the latest advancements in AI and ML to continuously enhance solutions.

Skills & Qualifications:

• 10+ years of experience in machine learning and software development.
• Proficiency in Python and strong programming skills in Java or Scala.
• In-depth knowledge of ML algorithms, deep learning, and NLP techniques.
• Experience deploying ML models on cloud platforms (AWS, Azure, or GCP).
• Familiarity with big data technologies for scalable data processing.
• Understanding of MLOps best practices for model deployment and monitoring.
• Strong problem-solving, analytical, and communication skills.
• Proactive, curious, and solution-oriented mindset, aligned with our core values of positivity, clear communication, and continuous learning.