This is not a research-only or GenAI exploration role. We are looking for an experienced practitioner who has **built and deployed AI/ML solutions in production**, can work closely with product and engineering teams, and can confidently communicate technical concepts and architecture decisions to customers and non-technical stakeholders.
Experience in the **energy domain is highly preferred**.
Key Responsibilities
* Design, develop, and deploy machine learning models for complex business problems.
* Apply traditional ML techniques including classification, regression, forecasting, anomaly detection, feature engineering, and model evaluation.
* Build and productionize LLM and Generative AI solutions.
* Develop **Agentic AI workflows** using technologies such as LangGraph, LangChain, MCP, tool calling, and agent orchestration.
* Design and implement production-grade **RAG solutions**, including vector databases, hybrid retrieval, reranking, and knowledge graphs.
* Work with AWS Bedrock or comparable enterprise GenAI cloud platforms.
* Analyze large and complex datasets using Python, SQL, and modern data platforms.
* Collaborate with product managers, engineers, customers, and cross-functional stakeholders.
* Communicate architecture decisions, model performance, trade-offs, and recommendations to both technical and non-technical audiences.
* Support production deployment, monitoring, evaluation, and continuous improvement of AI/ML systems.
* Mentor junior data scientists and contribute to technical best practices.
Required Skills
* 5--7 years of relevant Data Science / Machine Learning experience.
* Strong hands-on traditional ML/Data Science background.
* Strong Python and SQL skills.
* Experience with ML frameworks such as **Scikit-learn, XGBoost, CatBoost, TensorFlow, or PyTorch**.
* 12 months of **recent, continuous hands-on GenAI/LLM experience**.
* Proven production deployment of AI/ML or GenAI systems.
* Hands-on **Agentic AI production experience**, including one or more of:
