Description

Key Skills: Machine Learning, Python, Generative AI, Gen AI, AI/ML, LLM, ML Engineering, Spark, PySpark, SparkSQL, Apache Spark, Azure, AWS

Roles & Responsibilities:

• Develop and deploy agentic AI workflows to support business processes within the private equity value chain.
• Design, build, and optimize machine learning models and Generative AI solutions for enterprise applications.
• Implement advanced practices in context engineering and evaluation frameworks for AI agents.
• Research, train, fine-tune, and evaluate AI models to optimize performance and achieve defined objectives.
• Build scalable web services and APIs to support model inferencing and AI-driven applications.
• Develop and manage AI-ready data pipelines integrating structured and unstructured data sources.
• Work with tools, functions, and Model Context Protocol (MCP) services to support AI workflows.
• Ensure best practices in data quality, governance, security, and compliance.
• Collaborate with data scientists, engineers, and stakeholders to support modeling and application needs.
• Monitor and optimize performance of data and model pipelines, troubleshooting issues in real time.
• Contribute to improving system reliability, scalability, and efficiency in AI-powered platforms.

Experience Required:

• 5 - 8 years of experience in machine learning, AI/ML engineering, or data engineering roles.
• Strong programming experience in Python for machine learning and data processing.
• Hands-on experience with Generative AI, LLM-based applications, and AI model deployment.
• Experience working with big data technologies such as Apache Spark, PySpark, or SparkSQL.
• Familiarity with cloud platforms such as Azure or AWS.
• Experience building scalable data pipelines and AI inference services.
• Strong understanding of machine learning lifecycle, model evaluation, and optimization techniques.
• Experience working with cross-functional teams in data-driven environments.

Education: Any Graduation

Key Skills

Education

Any Graduate