Description

Design and deploy agentic AI workflows and scalable inference services for the private equity value chain. You will own the end-to-end lifecycle of machine learning models and Generative AI solutions, from data engineering to production deployment.

Responsibilities

• Develop and deploy agentic AI workflows to support business processes within the private equity value chain.
• Research, train, fine-tune, and evaluate LLMs and AI models to optimize performance and achieve defined objectives.
• Implement advanced context engineering and evaluation frameworks for AI agents.
• 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 using Apache Spark.

Required Skills

• 5-8 years of experience in machine learning, AI/ML engineering, or data engineering.
• Strong programming proficiency in Python for machine learning and data processing.
• Hands-on experience with Generative AI, LLM-based applications, and AI model deployment.
• Experience with big data technologies: Apache Spark, PySpark, and SparkSQL.
• Familiarity with cloud platforms (Azure or AWS).
• Experience building scalable data pipelines and AI inference services.
• Understanding of machine learning lifecycle, model evaluation, and optimization techniques.
• Experience working with Model Context Protocol (MCP) services.

Preferred Skills

• Experience ensuring data quality, governance, security, and compliance in AI workflows.
• Ability to troubleshoot and optimize performance of data and model pipelines in real time.

Key Skills

Education

Any Graduate