Senior Machine Learning Engineer

Position Description, Responsibilities and Requirements

Quantum AI Labs

We are embarking on an exciting journey to build
Quantum AI Labs
— an AI startup funded and owned by Quantum Capital Group (QCG), a leading provider of private equity, credit, and venture capital to the global energy and energy transition industry (with over $30Bn in capital under stewardship).

Quantum AI Labs
sits at the intersection of
artificial intelligence, private equity, and the energy transition
— developing
AI-driven platforms and solutions
to transform both QCG's operations and the broader energy and energy investment ecosystem.

Our mission:
accelerate digital transformation and AI adoption
across the energy private equity value chain.

We're focused on:

• Transforming the private equity value chain through advanced AI platforms and tools.
• Enhancing portfolio companies' performance with AI-driven solutions.
• Leveraging QCG's deep energy expertise with cutting-edge AI to unlock value, improve efficiency, and drive sustainable growth.

Position Summary:

Quantum AI Labs
is seeking a
Senior Machine Learning Engineer
to design and build Machine Learning, Generative and Agentic AI–powered products transforming the Private Equity and Energy sectors. With 5–8 years of experience, the ideal candidate will be proficient in the basics of Machine Learning, and adept at building and maintaining data pipelines that support AI/ML and analytics platforms. This role requires hands-on expertise with modern data engineering frameworks, web-service development, cloud-based ecosystems, and the application of Generative AI tools to accelerate software development workflows. The candidate should also bring experience with natural language processing (NLP) techniques to enrich and enhance data for advanced analytics and AI solutions.

Job Responsibilities:

• Develop and deploy agentic AI workflows for private equity value chain 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 optimize 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 modeling and application needs.
• Optimize performance of data and model pipelines and troubleshoot issues in real time.
• Collaborate with stakeholders to define data requirements and deliver reliable solutions.

Experience and Abilities:

• 5–8 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.
• Practical experience in applying NLP techniques for data enrichment and transformation.
• Familiarity with relational and NoSQL databases, and data warehousing solutions (MongoDB, Redshift, BigQuery).
• Knowledge of API integration, data streaming, and real-time processing.
• Strong problem-solving, analytical, and debugging skills.
• Bachelor's or Master's degree in Computer Science, Data Engineering, or related field.