Senior Data Scientist (GenAI & Utilities)

Hierarch Soft Technologies Inc; Corpus Christi (TX); Hybrid; USD 92,000 - 111,000; Full time

Job summary

Hierarch Soft Technologies Inc. seeks a Senior Data Scientist focused on GenAI and utility operations in Corpus Christi, TX.

This long-term contract role requires hands-on expertise in building practical generative AI capabilities, including LLM/RAG systems, data integration, and performance validation against source docs. You will work across data engineering, AI model deployment, and operations teams to design vector-based data pipelines, optimize retrieval, and ensure alignment with business

Qualifications

• Strong hands-on experience with Python, PyTorch or TensorFlow, and relational database architecture using SQL.
• Experience integrating enterprise frameworks using LangChain or LlamaIndex for transformation projects.
• Understanding of vector embeddings and the challenges of converting legacy unstructured files into high-quality semantic search vectors.
• Experience with schema design, data modeling, prompt engineering, semantic indexing, and search optimization.
• Ability to analyze complex unstructured formats and translate them into appropriate relational or vector database designs.
• Experience with data mapping, vectorization, output validation, reconciliation, and model evaluation testing.
• Strong troubleshooting and analytical problem-solving skills.
• Ability to work collaboratively with cross-functional technical and operational teams.
• Excellent communication skills, including the ability to explain complex AI concepts clearly.

Responsibilities

• Support integration and mapping of unstructured utility data into modern vector database systems.
• Review schemas, tables, relationships, embeddings, and vector database objects for the target environment.
• Analyze legacy data structures across asset management logs, SCADA alerts, and maintenance records for relational and vector formats.
• Develop and review source-to-target mappings, embedding rules, transformation logic, and ingestion procedures.
• Write, review, and optimize SQL queries, prompt structures, and related database code for retrieval and application functionality.
• Identify and address performance bottlenecks by analyzing vector search latency and indexing strategies.
• Run data integrity checks and evaluation testing, including hallucination checks and accuracy validation.
• Assist with deployment and pipeline integration planning, testing, and production scheduling.
• Troubleshoot RAG pipeline failures and data inconsistencies; coordinate with cross-functional teams.

Skills

Python

PyTorch

TensorFlow

SQL

LangChain

LlamaIndex

Vector embeddings

Data modeling

Schema design

Prompt engineering

Semantic search

RAG

Tools

Pinecone

Milvus

Weaviate

GPT-4

Llama 3

Job description

Long-term contract opportunity for a Senior Data Scientist focused on GenAI and utility operations in Corpus Christi, TX (hybrid). This role supports an AI task force building practical generative AI capabilities for operational excellence, including LLM and RAG system development, modernization of unstructured utility content, and performance and validation to keep results aligned to source documentation. The contract salary range is USD 91,900 to 111,000 per year.

What you’ll work on

• Support integration and mapping of unstructured utility data including technical manuals, maintenance logs, and field reports into modern vector database systems.
• Review existing document structures and help design schemas, tables, relationships, embeddings, and vector database objects for the target application environment.
• Analyze legacy data structures across asset management logs, SCADA alerts, and maintenance records to determine how to represent data in relational and vector formats.
• Develop and review source-to-target mappings, embedding rules, transformation logic, and ingestion procedures to ensure reliable vector database transfer.
• Write, review, and optimize SQL queries, prompt structures, and related database code needed for retrieval and application functionality.
• Identify and address performance bottlenecks by analyzing vector search latency, indexing strategies, semantic search query performance, and LLM orchestration settings.
• Run data integrity checks and evaluation testing, including hallucination checks and accuracy validation to verify generative AI outputs against source documentation and business requirements.
• Assist with deployment and pipeline integration planning, testing procedures, production staging, and implementation scheduling while minimizing disruption to ongoing operations.
• Troubleshoot RAG pipeline failures and system issues such as data inconsistencies, prompt formatting problems, model latency, and orchestration errors.
• Coordinate with software engineers, database administrators, data scientists, and other teams to understand dependencies between legacy systems and the target cloud environment (Azure/AWS).
• Recommend improvements to vector database design, data access patterns, and model architectures for maintainability, scalability, and performance.
• Maintain technical documentation for integration pipelines, RAG designs, prompt engineering guidelines, testing results, and model deployment procedures.
• Collaborate with field operations, engineering teams, project managers, and stakeholders to keep AI work aligned with project objectives, data needs, and technical standards.

What you bring

• Strong hands-on experience with Python, PyTorch or TensorFlow, and relational database architecture using SQL.
• Experience integrating enterprise frameworks using LangChain or LlamaIndex for transformation projects.
• Understanding of vector embeddings and the challenges of converting legacy unstructured files into high-quality semantic search vectors.
• Experience with schema design, data modeling, prompt engineering, semantic indexing, and search optimization.
• Ability to analyze complex unstructured formats and translate them into appropriate relational or vector database designs.
• Experience with data mapping, vectorization, output validation, reconciliation, and model evaluation testing.
• Strong troubleshooting and analytical problem-solving skills.
• Ability to work collaboratively with cross-functional technical and operational teams.
• Excellent communication skills, including the ability to explain complex AI concepts clearly.

Technology stack

Python, PyTorch, TensorFlow, SQL, LangChain, LlamaIndex, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), vector database systems, Pinecone, Milvus, Weaviate, GPT-4, Llama 3, Azure, AWS

Preferred qualifications

• Direct experience with vector database environments such as Pinecone, Milvus, or Weaviate.
• Experience designing and fine-tuning LLMs such as GPT-4 or Llama 3 for enterprise solutions.
• Background in system modernization and enterprise machine learning pipeline conversion projects.
• Familiarity with asset management systems, infrastructure data, SCADA logs, and industrial application dependencies where applicable.
• Experience planning AI model deployment testing, production cloud cutovers, and post-deployment validation in AWS or Azure.
• Knowledge of LLM performance tuning, token optimization, and context window management techniques.