AI/ML ENGINEER

We are looking for a skilled AI/ML Engineer with at least 3 years of experience to help design, develop, and deploy machine learning solutions across our products and services. The ideal candidate is proficient in data preprocessing, model building, evaluation, and deployment, with a strong foundation in algorithms and software engineering. You will work closely with cross-functional teams to translate business problems into data-driven solutions.

Key Responsibilities shall be to:

Design, develop, and deploy machine learning models for real-world applications.

• Analyze large datasets to extract insights, clean data, and engineer features.

• Experiment with different ML algorithms and techniques (e.g., supervised, unsupervised, deep learning).

• Collaborate with data engineers, product teams, and software developers.

• Monitor and maintain model performance and retraining pipelines in production.

• Optimize models for performance, scalability, and efficiency.

• Document processes, methodologies, and code for reusability and transparency.

• Stay updated on the latest research, tools, and industry trends in AI/ML.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, or a related field.

• Minimum of 3 years of experience in AI/ML model development and deployment.

• Proficiency in Python (preferred), with experience in ML libraries (e.g., scikit-learn, TensorFlow, PyTorch).

• Strong understanding of machine learning algorithms, neural networks, and statistical modeling.

• Experience with data manipulation tools (e.g., Pandas, NumPy) and SQL.

• Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and ML Ops tools (e.g., MLflow, Kubeflow).

• Solid understanding of software engineering practices including version control (Git), testing, and CI/CD pipelines.

• Ability to communicate technical concepts clearly to non-technical stakeholders.

Would be Nice-to-Have:

• Experience with natural language processing (NLP), computer vision, or recommendation systems.

• Exposure to large language models (e.g., GPT, BERT) and generative AI tools.

• Knowledge of model interpretability and fairness techniques.

• Published research or contributions to open-source ML projects.

Benefits

• A competitive base salary with performance-based incentives.