Senior Data Scientist – GenAI / Agentic AI

Job Summary

We are seeking a Senior Data Scientist with 5–7+ years of relevant experience and a strong foundation in traditional Data Science and Machine Learning, combined with hands-on experience building and deploying Generative AI, LLM, RAG, and Agentic AI solutions in production.

The ideal candidate will have experience solving complex business problems using machine learning, developing enterprise AI solutions, and working directly with customers and cross-functional teams. Strong communication and consulting skills are essential.

Key Responsibilities

• Develop and deploy machine learning models to solve complex business problems.
• Build predictive models for classification, regression, forecasting, anomaly detection, and other ML use cases.
• Perform data analysis, feature engineering, model evaluation, and statistical modeling.
• Design and implement LLM and Generative AI solutions for enterprise applications.
• Develop production-grade Agentic AI workflows using LangGraph, LangChain, MCP, tool calling, and agent orchestration.
• Design and implement RAG architectures, including vector databases, hybrid retrieval, reranking, and knowledge graphs.
• Build and deploy GenAI solutions using AWS Bedrock. Azure OpenAI or Google Vertex AI experience is acceptable as an alternative.
• Integrate AI/ML solutions into enterprise products and production environments.
• Work with large and complex datasets using SQL, Spark, Hadoop, and related technologies.
• Collaborate with product managers, software engineers, architects, and other stakeholders.
• Communicate technical findings, architecture decisions, and trade-offs clearly to technical and non-technical audiences.
• Participate in customer-facing discussions and provide technical guidance during solution development.
• Mentor junior data scientists and contribute to best practices across the team.
• Stay current with developments in Machine Learning, Generative AI, LLMs, and Agentic AI.

Required Skills

• 5–7+ years of relevant Data Science / Machine Learning experience
• Strong proficiency in Python or R
• Strong traditional ML/DS background
• Experience with classification, regression, forecasting, anomaly detection, feature engineering, and model evaluation
• Hands-on Generative AI / LLM experience
• 12+ months of recent, continuous hands-on GenAI/LLM experience
• Production experience with Agentic AI
• LangGraph, LangChain, MCP, tool calling, agent orchestration
• Strong RAG implementation experience
• Vector databases, hybrid retrieval, reranking, knowledge graphs
• AWS Bedrock experience
• Production deployment of AI/ML solutions
• Experience with Scikit-learn, TensorFlow, PyTorch, or similar ML frameworks
• Strong SQL and database experience
• Experience with Spark, Hadoop, or similar big-data technologies
• Strong statistical analysis and data modeling skills
• Excellent communication and stakeholder management skills

Preferred Skills

• Energy, Utilities, Oil & Gas, or Natural Resources domain experience
• Databricks
• Snowflake
• XGBoost / CatBoost
• LLMOps and AI evaluation tooling
• Experience with AWS, Azure, or Google Cloud
• Experience working with enterprise-level software products
• Master's or Ph.D. in Computer Science, Statistics, Mathematics, or another quantitative discipline

Candidate Profile

The ideal candidate should:

• Have real-world production experience, not just academic or proof-of-concept GenAI exposure.
• Demonstrate strong traditional ML/DS fundamentals alongside modern GenAI expertise.
• Have at least 12 months of recent hands-on LLM/GenAI work.
• Be comfortable interacting directly with customers and explaining complex technical concepts.
• Be able to discuss architecture decisions, implementation approaches, and technical trade-offs.
• Have experience taking AI/ML solutions from development through production deployment.

Domain Preference

Candidates with experience in the energy sector are highly preferred, particularly from: