ML / AI Engineer

AI ML Engineer

Job Description:
We are seeking a passionate and skilled Machine Learning / AI Engineer to join our team and help design, build, and deploy cutting-edge ML and AI solutions. The ideal candidate has a solid foundation in machine learning algorithms, model development, and data engineering, and is comfortable working in a fast-paced, collaborative environment.

Responsibilities:
Design and implement machine learning models for classification, regression, recommendation, NLP, or computer vision tasks.

Train, test, and evaluate models using large datasets, ensuring scalability and performance.

Deploy models to production using MLOps best practices (e.g., CI/CD pipelines, model versioning, monitoring).

Collaborate with data scientists, software engineers, and product teams to understand business requirements and deliver AI-driven solutions.

Perform data preprocessing, feature engineering, and statistical analysis to ensure high-quality model input.

Research and experiment with new algorithms and frameworks to improve model accuracy and performance.

Create clear technical documentation and communicate results to both technical and non-technical stakeholders.

Required Skills:
Strong understanding of machine learning algorithms (e.g., linear regression, decision trees, SVMs, neural networks, clustering).

Hands-on experience with Python and libraries such as scikit-learn, TensorFlow, PyTorch, XGBoost, or Keras.

Experience with data manipulation and analysis using NumPy, Pandas, and SQL.

Knowledge of deep learning, NLP, or computer vision concepts.

Familiarity with MLOps, model deployment, Docker, APIs, and cloud platforms (AWS/Google Cloud Platform/Azure).

Proficiency in version control tools (e.g., Git) and development best practices.

Nice-to-Have:
Experience with LLMs (Large Language Models) or Generative AI.

Exposure to tools like MLflow, Airflow, Kubeflow, or Vertex AI.

Background in statistics, data science, or software engineering.

Familiarity with big data tools like Spark, Hadoop, or Databricks.