What are the responsibilities and job description for the AI/ML Engineer position at Jobs via Dice?
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About the Role:
Looking for a hands-on Machine Learning Engineer who enjoys solving real business problems with AI. In this role, you ll work closely with data scientists, engineers, and commercial teams to design, build, and deploy machine learning solutions that create measurable value across trading, operations, and support functions.
What You ll Do:
About the Role:
Looking for a hands-on Machine Learning Engineer who enjoys solving real business problems with AI. In this role, you ll work closely with data scientists, engineers, and commercial teams to design, build, and deploy machine learning solutions that create measurable value across trading, operations, and support functions.
What You ll Do:
- Lead the design, development, and deployment of scalable ML systems
- Work across data collection, feature engineering, experimentation, and model tuning
- Implement production-grade solutions and integrate them into existing platforms and tools
- Contribute to GenAI initiatives, including LLM-based applications and use cases
- Participate in code reviews, tooling discussions, and experiment design
- Share knowledge and help build ML expertise across the organization
- Master s degree in computer science, Data Science, Machine Learning, or related field (PhD a plus)
- 5 7 years of industry experience building and deploying ML models in production
- Strong proficiency in Python and clean, maintainable coding practices
- Hands-on experience with frameworks such as TensorFlow, PyTorch, or Transformers
- Familiarity with AWS and containerization tools such as Docker
- Strong problem-solving abilities and comfort working independently or in teams
- Ability to translate complex technical concepts for non-technical audiences
- Solid understanding of ML fundamentals, including deep learning and NLP concepts
- Production experience implementing GenAI applications
- Background in energy or commodities trading or exposure to financial markets
- Experience integrating ML models into dashboards (Dash, Streamlit, etc.)
- Time-series modeling and applied ML across real-world datasets
- Knowledge of enterprise software practices (version control, CI/CD, system design)
- Experience with data orchestrators such as Airflow or Dagster and cloud ETL/ELT pipelines