Machine Learning Engineer

About the job

We are looking for a skilled and motivated Machine Learning Engineer with 2+ years of experience to design, develop, and implement machine learning models and systems. The ideal candidate will have a strong foundation in machine learning algorithms, software engineering, and a passion for solving complex problems with data-driven solutions.

Key Responsibilities:

• Develop, train, and optimize machine learning models for various use cases, including classification, regression, clustering, and deep learning.
• Design and implement scalable machine learning pipelines for data preprocessing, feature engineering, model training, and deployment.
• Collaborate with cross-functional teams to understand business requirements and translate them into technical solutions.
• Monitor and improve the performance of deployed models using appropriate evaluation metrics and feedback loops.
• Stay updated with the latest advancements in machine learning research and tools.
• Perform exploratory data analysis (EDA) and ensure data quality and integrity for machine learning projects.
• Deploy machine learning models to production using cloud platforms or on-premise infrastructure.

Required Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, or a related field.
• Must be familiar with RAG concepts and Agentic AI
• Strong programming skills in Python and experience with machine learning frameworks/libraries (e.g., TensorFlow, PyTorch, Scikit-learn).
• Proficiency in working with data manipulation and analysis tools such as Pandas and NumPy.
• Hands-on experience with machine learning algorithms (e.g., supervised, unsupervised, and reinforcement learning).
• Knowledge of deep learning architectures like CNNs, RNNs, and transformers.
• Familiarity with cloud platforms (e.g., AWS, Google Cloud, or Azure) and containerization tools (e.g., Docker, Kubernetes).
• Experience in version control systems such as Git.

Preferred Qualifications:

• Experience with big data technologies such as Spark, Hadoop, or Kafka.
• Knowledge of Natural Language Processing (NLP) and Computer Vision.
• Understanding of MLOps principles and tools for CI/CD in machine learning pipelines.
• Experience with RESTful APIs and integration with machine learning models.
• Strong problem-solving and analytical skills.