AI/ML Engineer

As an AI/ML Engineer with 3 to 5 years of professional experience, you will play a crucial role in bridging the gap between data science and production engineering. Your primary responsibility will be to design, build, deploy, and maintain scalable machine learning models and pipelines that power our core products. Here is a breakdown of your key responsibilities: - Model Development & Engineering: - Design, develop, and train robust machine learning and deep learning models to solve complex business problems. - Optimize and fine-tune existing models and architectures for improved accuracy, speed, and resource efficiency. - Implement state-of-the-art algorithms in areas such as Natural Language Processing (NLP), Computer Vision, or Predictive Analytics based on project needs. - MLOps & Pipeline Architecture: - Build, maintain, and automate end-to-end data and ML pipelines (data ingestion, preprocessing, training, evaluation, and deployment). - Deploy ML models as scalable APIs or microservices within containerized environments. - Monitor production model performance, implement logging, and set up automated retraining loops to handle data drift. - Collaboration & Software Best Practices: - Collaborate closely with Data Scientists, Data Engineers, and Product Managers to translate business requirements into technical solutions. - Write clean, maintainable, well-documented, and production-ready code. - Participate in code reviews, mentoring junior engineers, and championing software engineering best practices (CI/CD, testing, version control). In addition to the above responsibilities, you should possess the following qualifications: - Experience: 35 years of professional experience as an AI/ML Engineer, Software Engineer (focused on ML), or Data Engineer in a production environment. - Education: Bachelors or Masters degree in Computer Science, Data Science, Statistics, Mathematics, or a related technical field. - Programming: Mastery of Python and familiarity with standard libraries (NumPy, Pandas, Scikit-Learn). Knowledge of C++ or Java is a plus. - Frameworks: Deep hands-on experience with modern deep learning frameworks, specifically PyTorch or TensorFlow. - MLOps & Tools: Proficient with MLOps tools (e.g., MLflow, Kubeflow, Weights & Biases) and workflow orchestration (e.g., Airflow). - Cloud & DevOps: Strong experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes). - Data Architecture: Solid understanding of SQL and NoSQL databases, as well as big data processing tools (e.g., Spark). Preferred qualifications include experience with Large Language Models (LLMs), edge AI deployment, model quantization techniques, and contributions to open-source AI/ML projects or a strong portfolio on GitHub. As an AI/ML Engineer with 3 to 5 years of professional experience, you will play a crucial role in bridging the gap between data science and production engineering. Your primary responsibility will be to design, build, deploy, and maintain scalable machine learning models and pipelines that power our core products. Here is a breakdown of your key responsibilities: - Model Development & Engineering: - Design, develop, and train robust machine learning and deep learning models to solve complex business problems. - Optimize and fine-tune existing models and architectures for improved accuracy, speed, and resource efficiency. - Implement state-of-the-art algorithms in areas such as Natural Language Processing (NLP), Computer Vision, or Predictive Analytics based on project needs. - MLOps & Pipeline Architecture: - Build, maintain, and automate end-to-end data and ML pipelines (data ingestion, preprocessing, training, evaluation, and deployment). - Deploy ML models as scalable APIs or microservices within containerized environments. - Monitor production model performance, implement logging, and set up automated retraining loops to handle data drift. - Collaboration & Software Best Practices: - Collaborate closely with Data Scientists, Data Engineers, and Product Managers to translate business requirements into technical solutions. - Write clean, maintainable, well-documented, and production-ready code. - Participate in code reviews, mentoring junior engineers, and championing software engineering best practices (CI/CD, testing, version control). In addition to the above responsibilities, you should possess the following qualifications: - Experience: 35 years of professional experience as an AI/ML Engineer, Software Engineer (focused on ML), or Data Engineer in a production environment. - Education: Bachelors or Masters degree in Computer Science, Data Science, Statistics, Mathematics, or a related technical field. - Programming: Mastery of Python and familiarity with standard libraries (NumPy, Pandas, Scikit-Learn). Knowledge of C++ or Java is a plus. - Frameworks: Deep hands-on experience with modern deep learning frame