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We are looking for a Senior AI Engineer who can translate business problems into scalable analytical solutions and lead the design of AI-driven solutions. You will work at the intersection of data, domain, and decision-making, collaborating with engineering and strategy teams to operationalize machine learning in real-world contexts.
Key Responsibilities
- Lead end-to-end AI driven initiatives — from problem framing and data exploration to model deployment.
- Build and optimize predictive, prescriptive, and generative models using modern ML techniques.
- Partner with data engineering teams to ensure robust data pipelines and governance for model reliability.
- Define and implement model evaluation frameworks — accuracy, drift detection, explainability, and impact metrics.
- Mentor junior data scientists and analysts; establish coding and experimentation best practices.
- Collaborate with business stakeholders to identify high-value AI use cases and prototype proofs of concept.
Required Skills & Experience
- 10+ years of total experience, with 5+ years in applied data science or machine learning.
- Expertise in Python, SQL, and ML libraries (scikit-learn, XGBoost, PyTorch, TensorFlow).
- Strong foundation in statistics, optimization, and data storytelling.
- Experience with cloud environments (AWS, Azure, or GCP) and MLOps frameworks (SageMaker, MLflow, Kubeflow).
- Exposure to LLMs, NLP, or Generative AI for applied use cases.
- Ability to translate domain-specific challenges into measurable data science outcomes — ideally in energy, commodities, or financial analytics.
- Excellent communication and mentoring abilities; comfortable working in client-facing roles.
Nice to Have
- Experience integrating data science into data platforms or governance frameworks.
- Prior experience in consulting or product environments.
