Data Engineer, Analytics

Location: Irving, Tx

Assignment Type: 6 Month Contract-to-hire

Compensation: 125k-150k

Work Model: 4 days on-site, 9:00am-5:00pm

The Full-Stack AI/ML Engineer will work on high-impact initiatives supporting the company's digital transformation in the electrical distribution space.

Building and implementing AI-powered chatbots for companys customer-facing applications to streamline communication, enhance user experience, and improve service efficiency.

Designing and integrating machine learning models focused on pricing optimization and sales forecasting , helping the business make data-driven decisions and improve profitability.

Collaborating cross-functionally with product, data, and engineering teams to bring AI models from prototype to production within companys technology ecosystem.

Qualifications

Ideal candidates will have experience across the full AI/ML development stack — from data preparation and model development to application integration and deployment — and be excited to apply their technical expertise in a fast-moving, customer-focused business environment.

Responsibilities

Design and build AI/ML solutions that automate, optimize, or enhance business workflows.

Acquire and preprocess structured/unstructured data from diverse sources (APIs, databases, OCR pipelines, documents, etc.).

Conduct Exploratory Data Analysis (EDA) and develop statistical and predictive models using Python and ML frameworks.

Build and fine-tune Large Language Model (LLM) pipelines (e.g., OpenAI, Azure OpenAI, Hugging Face, LangChain).

Implement retrieval-augmented generation (RAG) and document‑intelligence systems.

Develop and deploy production‑grade APIs and microservices using FastAPI or similar, integrated with MLOps practices.

Collaborate with data engineers to ensure efficient data pipelines and with software engineers to integrate models into products.

Continuously monitor, retrain, and optimize deployed models.

Research and prototype emerging AI methods—multimodal models and AI agents.

Document architecture, design choices, and experiment outcomes for transparency and reproducibility.

Work as a core member of a cross‑functional AI team, contributing to sprint planning, backlog grooming, daily stand‑ups, and retrospectives under Scrum / Agile frameworks.

Participate in peer code reviews, ensure clean coding practices, and contribute to shared libraries and internal AI frameworks.

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