AIML

Our challenge

As an AI/ML Developer/Lead/Architect candidate will be responsible for building testing and deploying AI/ML models that can analyze and interpret complex data sets and enable predictive insights. Candidate will work closely with our product development data science and engineering teams to create solutions that deliver realworld impact.

The Role

Responsibilities:

• Develop and implement AI/ML models: Design develop test and deploy machine learning models that solve realworld problems. Candidate will handle data preprocessing model training tuning and evaluation.
• Data Exploration and Analysis: Work with structured and unstructured data to prepare it for ML models. Collaborate with data engineers to ensure high data quality and availability.
• Model Optimization: Optimize algorithms to ensure high accuracy and low latency. Use techniques like hyperparameter tuning regularization and transfer learning.
• Collaborate Across Teams: Work with product managers engineers and data scientists to integrate AI solutions into the companys offerings. Provide technical expertise and share knowledge with team members.
• Maintain and Update Models: Monitor the performance of deployed models make improvements as needed and ensure their reliability and scalability.
• Stay Updated: Keep up with the latest AI/ML advancements tools and libraries to bring innovative approaches to the team.

Requirements:

You are:

• Bachelors or Masters degree in Computer Science Data Science Engineering or a related field.
• Minimum of 10 years of experience in AI/ML model development deployment and optimization.
• Proficiency in Python R or similar programming languages.
• Experience with machine learning libraries like TensorFlow PyTorch Scikitlearn etc.
  Strong knowledge of ML algorithms including supervised unsupervised and reinforcement learning.
• Familiarity with cloud platforms (AWS Azure Google Cloud) for deploying models.
  Understanding of data preprocessing techniques and experience with SQL and NoSQL databases.
• Analytical Skills: Excellent problemsolving skills and a datadriven mindset.
• Communication: Ability to explain technical concepts to nontechnical stakeholders effectively.

It would be great if you also had:

• Experience with natural language processing computer vision or recommendation systems.
• Familiarity with big data technologies (e.g. Spark Hadoop) and data visualization tools.
• Understanding of MLOps and experience with tools like MLflow or Kubeflow.
  Knowledge of Agile methodologies.