Advanced AI Scientist

HP•Houston, TX

About The Position

Advanced AI Scientist Description - This role is responsible for enabling innovation and creativity by bringing cutting edge perspectives on adopting latest data mining and modelling techniques. The role understands current complex business problems and future business strategy to assess, build and deploy required data mining and modelling capabilities. The role is involved in driving standardization, productivity and cross team learning by establishing processes and SOPs for entire data model development lifecycle. The role drives excellence through continuous improvement in model accuracy and reliability. Responsibilities Responsible for the architecture and technical leadership of the end-to-end AI ecosystem including multimodal data pipelines, LLM-based engineering copilots, agentic automation systems, and predictive models for product development monitoring. Develop the full stack AI solution (LLMs, ML, agents, APIs, pipelines). Design multi-agent workflows for automated monitoring, anomaly detection & reporting. Establish LLM fine-tuning, evaluation, RAG, Semantic modelling(NLP/AI)and prompt engineering frameworks. Integrate engineering domain data (mechanical, electrical, field, test logs). Define MLOps, model governance, observability, and monitoring strategies. Collaborate with product development & engineering teams to map use cases.

Requirements

AI/ML
AI Architecture & LLM
Large Language Models (fine-tuning, RAG, distillation)
Agentic AI frameworks (OpenAI agents, LangGraph, AutoGen, CrewAI)
Multimodal AI (text + time-series + telemetry + image)
Knowledge Graphs (Neo4j, RDF, GraphRAG)
End-to-end pipeline design
Vector DBs (Pinecone, Milvus, FAISS)
Cloud AI (Azure AI Studio, AWS)
Microservices & API integration
Machine Learning and Deep Learning
ReactJS, Java Script, Streamlit, Flask
MLOps
MLflow, Kubeflow, Airflow
Model monitoring, evaluation, CI/CD
Domain Understanding product development lifecycle
Familiarity with mechanical/electrical engineering data formats
Four-year or Graduate Degree in Mathematics, Statistics, Economics, Computer Science, Data Science, or any other related discipline or commensurate work experience or demonstrated competence.
Typically, has 7-10 years of work experience, preferably in data analytics, statistical modeling, machine learning, or a related field.

Nice To Haves

Programming Language/s Certification (SQL, Python, or similar)
Algorithms
Amazon Web Services
Apache Spark
Artificial Intelligence
Big Data
Computer Science
Data Analysis
Data Mining
Data Science
Data Visualization
Natural Language Processing
Predictive Modeling
Python (Programming Language)
SQL (Programming Language)
Statistical Modeling
Statistics
Power Platforms - PowerBI, PowerApps
Cross-Org Skills
Effective Communication
Results Orientation
Learning Agility
Digital Fluency
Customer Centricity

Responsibilities

Responsible for the architecture and technical leadership of the end-to-end AI ecosystem including multimodal data pipelines, LLM-based engineering copilots, agentic automation systems, and predictive models for product development monitoring.
Develop the full stack AI solution (LLMs, ML, agents, APIs, pipelines).
Design multi-agent workflows for automated monitoring, anomaly detection & reporting.
Establish LLM fine-tuning, evaluation, RAG, Semantic modelling(NLP/AI)and prompt engineering frameworks.
Integrate engineering domain data (mechanical, electrical, field, test logs).
Define MLOps, model governance, observability, and monitoring strategies.
Collaborate with product development & engineering teams to map use cases.