**Position: AI Engineer (Advanced / GenAI & Production AI Focus)

Location: Dallas TX (On-site / Hybrid)

Type Contract

Job Description-

We are looking for a highly skilled AI Engineer to design, build, and deploy production-grade AI

systems, including LLM-powered applications, predictive models, and intelligent automationsolutions. This role focuses on technical execution, scalability, and performance optimization,

working closely with FDEs and business stakeholders.

Key Responsibilities

AI/ML Model Development
Develop models for:
Forecasting (demand, supply chain)
Predictive maintenance
Anomaly detection
Optimization problems
Implement deep learning using PyTorch / TensorFlow.
Generative AI & LLM Systems
Build and deploy:
RAG (Retrieval Augmented Generation) systems
Embedding pipelines
AI agents and copilots
Integrate LLM APIs (OpenAI, Azure OpenAI, etc.)
Optimize prompt engineering and inference pipelines.
Production Deployment (MLOps)
Deploy models via:
REST APIs
Streaming pipelines
Batch inference systems
Implement CI/CD for ML pipelines
Handle model versioning, monitoring, retraining
Data Engineering Collaboration
Work with FDEs to integrate AI models into:
Data pipelines
Enterprise systems
Process structured + unstructured data efficiently
Real-Time AI Systems
Build low-latency inference systems
Optimize model serving for scalability and performance
Support real-time decision systems
Domain-Focused AI Solutions

Develop AI For

Manufacturing optimization
Supply chain forecasting
Process automation
Quality inspection (computer vision, if required)

Technical Skills Required

AI/ML

Strong ML fundamentals
Deep learning (CNNs, RNNs, Transformers)
Time-series analysis

GenAI

LLMs, embeddings, vector DBs (FAISS, Pinecone, etc.)
RAG pipelines and prompt engineering

Programming

Python (advanced)
NumPy, Pandas, Scikit-learn
MLOps & Deployment
Docker, Kubernetes
MLflow / Azure ML / SageMaker

Data & Systems

SQL + big data tools
Spark / Kafka (good to have)

Experience Required

3 10 years (depending on level)
Experience deploying production AI systems
Exposure to real-time or enterprise AI solutions
Success Metrics
Model performance and accuracy
Reliability and scalability of deployed systems
Adoption of AI solutions in business workflows
Speed of deployment (POC Production)