Machine Learning Engineer II

• Work in a cross-functional geographically distributed team of Machine Learning engineers and ML Scientists to design and code large scale batch and a few real-time data pipelines on the Cloud.
• Prototype creative solutions quickly by developing minimum viable products and work with seniors and peers in crafting and implementing the technical vision of the team
• Actively participate in all phases of the end-to-end ML model lifecycle (includes feature engineering, model training, model scoring, model validation) for enterprise applications projects to tackle sophisticated business problems in production environments
• Collaborate with global team of data scientists, administrators, data analysts, data engineers, and data architects on production systems and applications
• Collaborate with cross-functional teams to integrate generative AI solutions into existing workflow systems.
• Participate in code reviews to assess overall code quality and flexibility.
• Passionate about simplifying systems, processes, and architecture while maintaining high quality and reliability.
• Define, develop and maintain artifacts like technical design or partner documentation
• Maintain, monitor, support and improve our solutions and systems with a focus on service excellence

Minimum Qualifications:

• A degree in software engineering, computer science, informatics, or a similar field.
• 3+ years of professional experience with a Bachelor's degree, or 2+ years with a Master's degree.
• Must have experience in big data technologies, particularly Spark, Hive, and Databricks.
• Proficiency in Python and experience developing and deploying Batch and Real-Time Inferencing applications.
• Good understanding of prompt engineering, Retrieval-Augmented Generation (RAG), AI agents, and LLM orchestration frameworks.

Preferred Qualifications:

• Experience programming in Scala.
• Hands-on experience with OOAD, design patterns, SQL, and NoSQL.
• A good understanding of machine learning pipelines and the ML Lifecycle.
• Experience integrating GenAI solutions with enterprise systems, APIs, and data platforms to solve business problems.
• Experience using cloud services (e.g., AWS) and workflow orchestration tools (e.g., Airflow).
  • Familiarity with the basics of both traditional Machine Learning and Generative-AI algorithms and tools.