Machine Learning Engineer
Posted Updated
Description
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.
