Job
Description
Role Overview: As an AI/ML Engineer, your role will involve designing, building, and deploying end-to-end AI/ML systems in production. You will be required to demonstrate expertise in data science, including statistical analysis, experimentation, and data storytelling. Additionally, you will work with large-scale, real-world datasets for model training and analysis. Your strong leadership experience will be essential in building and guiding high-performing AI teams. Key Responsibilities: - Design, build, and deploy end-to-end AI/ML systems in production. - Demonstrate expertise in data science, statistical analysis, experimentation, and data storytelling. - Work with large-scale, real-world datasets for model training and analysis. - Solve complex ML problems independently, from idea to implementation. - Build and guide high-performing AI teams. - Hands-on experience with deep learning, NLP, LLMs, and classical ML techniques. - Fluency in model experimentation, tuning, optimization, and evaluation at scale. - Comfort with software engineering and working with data and full-stack teams. - Experience with cloud platforms (GCP, AWS, Azure) and production-grade ML pipelines. - Bias for action, willing to jump into code, data, or ops to ship impactful AI products. Qualification Required: - PhD in Computer Science, Data Science, Machine Learning, AI, or related field. - Strong programming skills in Python (preferred), with experience in Java, Scala, or Go a plus. - Deep expertise with ML frameworks like TensorFlow, PyTorch, Scikit-learn. - Experience with large-scale datasets, distributed data processing (e.g. Spark, Beam, Airflow). - Solid foundation in data science: statistical modeling, A/B testing, time series, and experimentation. - Proficiency in NLP, deep learning, and working with transformer-based LLMs. - Experience with MLOps practices CI/CD, model serving, monitoring, and lifecycle management. - Hands-on experience with cloud platforms (GCP, AWS, Azure) and tools like Vertex AI, SageMaker, or Databricks. - Strong grasp of API design, system integration, and delivering AI features in full-stack products. - Comfortable working with SQL/NoSQL and data warehouses like BigQuery or Snowflake. - Familiarity with ethical AI, model explainability, and secure, privacy-aware AI development. Role Overview: As an AI/ML Engineer, your role will involve designing, building, and deploying end-to-end AI/ML systems in production. You will be required to demonstrate expertise in data science, including statistical analysis, experimentation, and data storytelling. Additionally, you will work with large-scale, real-world datasets for model training and analysis. Your strong leadership experience will be essential in building and guiding high-performing AI teams. Key Responsibilities: - Design, build, and deploy end-to-end AI/ML systems in production. - Demonstrate expertise in data science, statistical analysis, experimentation, and data storytelling. - Work with large-scale, real-world datasets for model training and analysis. - Solve complex ML problems independently, from idea to implementation. - Build and guide high-performing AI teams. - Hands-on experience with deep learning, NLP, LLMs, and classical ML techniques. - Fluency in model experimentation, tuning, optimization, and evaluation at scale. - Comfort with software engineering and working with data and full-stack teams. - Experience with cloud platforms (GCP, AWS, Azure) and production-grade ML pipelines. - Bias for action, willing to jump into code, data, or ops to ship impactful AI products. Qualification Required: - PhD in Computer Science, Data Science, Machine Learning, AI, or related field. - Strong programming skills in Python (preferred), with experience in Java, Scala, or Go a plus. - Deep expertise with ML frameworks like TensorFlow, PyTorch, Scikit-learn. - Experience with large-scale datasets, distributed data processing (e.g. Spark, Beam, Airflow). - Solid foundation in data science: statistical modeling, A/B testing, time series, and experimentation. - Proficiency in NLP, deep learning, and working with transformer-based LLMs. - Experience with MLOps practices CI/CD, model serving, monitoring, and lifecycle management. - Hands-on experience with cloud platforms (GCP, AWS, Azure) and tools like Vertex AI, SageMaker, or Databricks. - Strong grasp of API design, system integration, and delivering AI features in full-stack products. - Comfortable working with SQL/NoSQL and data warehouses like BigQuery or Snowflake. - Familiarity with ethical AI, model explainability, and secure, privacy-aware AI development.
