We're seeking a talented Machine Learning (ML) Engineer to design, develop, and deploy machine learning models and other innovative solutions. As an ML Engineer, you will work closely with an AI Architect and cross-functional teams to build intelligent systems that solve complex business problems and drive customer satisfaction.

Key Responsibilities:

Model Development: Design, implement, and train machine learning models using state-of-the-art algorithms and frameworks such as TensorFlow, PyTorch, and scikit-learn.
Data Preparation: Process, clean, and transform large datasets for training and evaluation of ML models.
Feature Engineering: Identify and engineer relevant features to optimize model performance and accuracy.
Algorithm Optimization: Research and implement advanced algorithms to address specific use cases, including classification, regression, clustering, and anomaly detection.
Integration: Collaborate with software developers to integrate ML models into production systems and ensure seamless operation.
Performance Evaluation: Evaluate model performance using appropriate metrics and continuously optimize for accuracy, efficiency, and scalability.
MLOps: Assist in setting up and managing CI/CD pipelines for model deployment and monitoring in production environments.
Research and Development: Stay updated with the latest advancements in AI/ML technologies and propose innovative solutions.
Collaboration: Work closely with data engineers, product teams, and stakeholders to understand requirements and deliver tailored ML solutions.

Qualifications:

Educational Background:

Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, or a related field.

Experience:

3 to 6 years of hands-on experience in developing and deploying machine learning models.

Technical Skills:

Strong proficiency in Python and ML libraries/frameworks (e.g., scikit-learn, TensorFlow, PyTorch).
Experience with data manipulation tools like Pandas, NumPy, and visualization libraries such as Matplotlib or Seaborn.
Familiarity with big data frameworks (Hadoop, Spark) is a plus.
Knowledge of SQL/NoSQL databases and data pipeline tools (e.g., Apache Airflow).
Experience with cloud platforms (AWS, Azure, Google Cloud) and their AI/ML services.
Strong understanding of supervised and unsupervised learning, deep learning, and reinforcement learning.
Exposure to MLOps practices and model deployment pipelines.

Soft Skills:

Strong problem-solving and analytical skills.
Effective communication and teamwork abilities.
Ability to work in a fast-paced, collaborative environment.