About the role

Responsibilities:

Develop and deploy agentic AI workflows for private equity value chains at Quantum.
This involves combining financially focused workflows with knowledge from the energy industry.
Implement state-of-the-art practices in context engineering for JTBD-focused AI agents, including evals for agents.
Research, train and tune AI agents to optimise eval metrics for specific objectives, including research on evals for qualitative tasks.
Work in tools, functions and MCP services to augment AI-ready data pipelines.
Develop/tune web services to scale model inferencing.
Integrate data from multiple structured and unstructured sources into unified data platforms.
Implement best practices for data quality, governance, security, and compliance.
Work with data scientists and software engineers to ensure seamless integration of data for modelling and application needs.
Optimise the performance of data and model pipelines and troubleshoot issues in real time.
Collaborate with stakeholders to define data requirements and deliver reliable solutions.

Requirements:

4+ years of experience in machine learning engineering or related roles.
Proficiency in using generative AI tools for accelerating software development and data workflows.
Hands-on experience in evaluating ML model metrics and tuning existing models.
Hands-on experience in developing web services (using any stack/framework).
Preference for web services performing model inference.
Hands-on expertise with data pipeline frameworks (Apache Airflow, Spark, Kafka, etc. ).
Strong programming skills in Python, SQL, Java, or Scala.
Experience with cloud-based data platforms (AWS, Azure, or GCP).
Proficiency in using generative AI tools for accelerating software development and data workflows.
Knowledge of API integration, data streaming, and real-time processing.
Strong problem-solving, analytical, and debugging skills.
A bachelor's or master's degree in computer science, data engineering, or a related field.

Machine Learning Engineer