Job Details

Data Scientist

Brooksource

Fortune 500 Oil & Gas Client

Houston

Overview

Our client's Digital Solutions team is expanding its advanced analytics and AI capabilities to support major enterprise initiatives—including the BAP AI Program, Sales Enablement AI, and emerging agentic workflow automation projects. With current headcount limited to two scientists, the team is building a strong bench of high-performance Data Scientists skilled in GenAI, enterprise ML engineering, and production-scale model deployment.

This role is ideal for a senior-level Data Scientist with hands-on experience developing, fine-tuning, and deploying models—especially LLM, RAG, and agentic systems—within complex industrial or enterprise environments.

What You’ll Do

• Design & deliver GenAI solutions: Architect and implement LLM/LVM applications (text and vision), including prompt strategies, guardrails, evaluation frameworks, and production rollout.
• Build robust RAG systems: Develop end-to-end RAG pipelines (ingestion → chunking → embedding → retrieval → synthesis) with observability, feedback loops, and AB testing for groundedness and hallucination reduction.
• Fine-tune foundation models: Adapt and fine-tune open-source or hosted LLMs using LoRA/QLoRA or other parameter-efficient methods; manage evaluation datasets and benchmark performance.
• Develop agentic workflows: Implement multi-step, tool-using AI agents with planning, memory, tool-calling, and safe execution policies.
• ML/LLM engineering: Build high-quality Python libraries, APIs, and production workflows with CI/CD, automated testing, experiment tracking, and model governance.
• Data & platforms: Collaborate with Data Engineering to operationalize pipelines on Databricks/Snowflake and integrate models with enterprise cloud AI/ML services.

Must-Have Skills

• Python (advanced; architecture, packaging, testing, performance)
• Machine Learning Engineering using Databricks or MLflow
• Built & deployed models in enterprise production environments
• LLM or RAG experience, including prior fine-tuning of any LLM
• Industrial or complex enterprise industry experience (strong preference)

Nice-to-Have Skills

• Oil & Gas or Industrial domain background
• Experience at large/enterprise companies
• Master’s degree (preferred)

Required Qualifications

Mathematics & ML Foundations:

• Strong knowledge of Statistics, Linear Algebra, Calculus, and Optimization
• Ability to explain trade-offs in model design and behavior

Programming:

• Advanced Python (typing, async, clean software architecture)
• API and library development

Python Ecosystem:

• Data: pandas, NumPy, Matplotlib/Seaborn, PySpark
• ML: scikit-learn, XGBoost, LightGBM
• Deep Learning: PyTorch or TensorFlow
• Generative AI: LangChain, LlamaIndex, Haystack, Hugging Face

Generative AI Expertise:

• Experience with multiple LLM/LVM providers (GPT, Claude, Gemini, Llama, etc.)
• Proven foundation model fine-tuning experience
• End-to-end RAG system development
• Prompt engineering and multi-step agentic workflows
• Embedding models and vector search systems

Software Craftsmanship & Platforms:

• Git (GitHub/GitLab/Bitbucket)
• SQL + NoSQL databases
• Cloud ML platforms: AWS, Azure, or GCP (SageMaker, Azure ML, Vertex)
• Databricks or Snowflake
• Azure AI Foundry (required)

Preferred Qualifications

• Vector Stores: FAISS, Milvus, Pinecone, Weaviate; hybrid retrieval
• LLMOps/Observability: MLflow, LangSmith, OpenTelemetry, RAGAS/DeepEval
• Orchestration: Airflow, Prefect
• Data Engineering: Kafka, Delta, Parquet, Unity Catalog
• Containers & Infra: Docker, Kubernetes, GitHub Actions, Azure DevOps, Terraform
• Safety & Governance: content moderation, jailbreak defense, PII protection
• MLOps Patterns: shadow/canary deployments, AB testing, blue-green rollouts
• Domain: Energy, industrial IoT, manufacturing, or similar data-rich environments