Python AI/ML Engineer (ONLY W2)

Job Title: Python AI/ML Engineer

Location: Irving, TX

AI/ML Engineer

Strong Python, LLM and Prompt engineering expertise with Java

Role Overview

• We are looking for a Senior AI/ML Engineer with strong hands-on experience across Machine Learning, Deep Learning, MLOps, Generative AI, and Agentic AI.

• The candidate will be responsible for designing, developing, deploying, and scaling AI solutions for enterprise use cases.

Key Responsibilities

• Design and develop end-to-end Machine Learning and Deep Learning solutions.

• Build and productionize ML models using appropriate algorithms and frameworks.

• Develop and manage MLOps pipelines covering model training, deployment, monitoring, and lifecycle management.

• Build Generative AI applications using LLMs, prompt engineering, embeddings, RAG, and vector databases.

• Design and implement Agentic AI solutions, including AI agents, tool calling, multi-agent workflows, and orchestration.

• Integrate AI solutions with enterprise APIs, databases, applications, and knowledge sources.

• Deploy scalable AI solutions on AWS, Azure, or Google Cloud Platform.

• Implement AI application monitoring, evaluation, observability, security, and responsible AI practices.

• Collaborate with product, engineering, data, and business teams to translate business problems into AI solutions.

• Mentor junior engineers and contribute to AI architecture and technology strategy.

Skill Requirements

Core Technical Skills

• Programming: Python, SQL

• Machine Learning: Scikit-learn, XGBoost/LightGBM

• Deep Learning: PyTorch / TensorFlow

• GenAI: LLMs, Prompt Engineering, Embeddings, RAG

• Agentic AI: LangGraph, LangChain or equivalent frameworks

• Vector Search: OpenSearch, FAISS, Pinecone or equivalent

• MLOps: MLflow, Docker, CI/CD, model deployment & monitoring

• Cloud: AWS / Azure / Google Cloud Platform

• APIs & Engineering: REST APIs, FastAPI, Git, containerization

Good to Have

• MCP and A2A protocols

• Multi-agent systems

• LLM and RAG evaluation

• AI observability and guardrails

• Kubernetes

• Knowledge Graph / Graph RAG

• Experience with enterprise-scale AI applications

Candidate Profile

• The ideal candidate should be a hands-on AI engineer who can take an AI use case from problem definition through development, deployment, and production monitoring.
• Strong problem-solving, communication, system-design, and mentoring skills are expected.