Sr. Ai Engineer

• Lead the design, development, and deployment of scalable AI and machine learning models to solve complex business challenges and deliver measurable value.
• Architect and maintain robust, efficient data pipelines for data ingestion, transformation, and preparation to support AI and machine learning workflows.
• Design and implement scalable data architectures and infrastructure that ensure high performance, reliability, and security for AI solutions.
• Collaborate with DevSecOps teams to integrate AI systems into production environments, ensuring compliance with security, performance, and scalability requirements.
• Develop and optimize distributed data systems, ensuring seamless data flow and processing for AI-driven applications.
• Champion MLOps practices, streamlining the deployment, monitoring, and lifecycle management of machine learning models.
• Apply advanced software engineering principles to integrate AI models into production systems, ensuring maintainability, modularity, and scalability.
• Evaluate and monitor the performance of AI models and data systems, implementing strategies for fine-tuning, retraining, and continuous improvement.
• Collaborate with cross-functional teams, including data engineers, software engineers, and stakeholders, to define and prioritize AI and data solutions aligned with business goals.
• Stay ahead of industry trends in AI/ML, GenAI, data engineering, MLOps, and other relevant technologies, adopting best practices and cutting-edge technologies.
• Mentor junior engineers and data scientists, fostering a culture of learning, innovation, and technical excellence.
• Contribute to the company’s thought leadership by publishing technical papers, filing patents, or presenting at industry events.

Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Data Science, AI, Machine Learning, or a related field
• Experience: 5+ years in building and deploying machine learning models and AI systems in production.
• Programming: Proficiency in Python, R, or Java, with experience in AI frameworks like TensorFlow, PyTorch, and Scikit-learn.
• Data Engineering: Strong knowledge of data pipelines, ETL processes, and tools like Apache Airflow, Kafka, and Spark.
• MLOps: Experience with containerization (Docker) and CI/CD pipelines.
• Cloud: Proficiency in cloud platforms (AWS, GCP, or Azure) and their data services.
• Infrastructure: Familiarity with distributed data systems (e.g., Hadoop, Snowflake) and scalable data architectures.
• Machine Learning: Expertise in machine learning and deep learning models.
• Soft Skills: Strong problem-solving, communication, and mentoring skills to lead and collaborate with teams effectively.