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:
LangGraph
LangChain
MCP
Tool calling
Agent orchestration
Strong RAG implementation experience, including:
Vector databases
Hybrid retrieval
Reranking
Knowledge graphs
AWS Bedrock experience strongly preferred.
Azure OpenAI or Google Vertex AI may be considered as alternate GenAI cloud experience.
Experience with big data technologies such as Spark/Hadoop.
Experience with Databricks and/or Snowflake is preferred.
Experience with LLMOps, evaluation, monitoring, or AI evaluation tooling is a plus.
Strong communication and client-facing/consulting skills.
Preferred Domain Experience