Senior GenAI Engineer: Build Scalable AI Apps & LLMs

Machine Learning Technologies, LLC; Plano (TX); On-site; USD 150,000 - 210,000; Full time

Job summary

Machine Learning Technologies, LLC seeks a Senior AI Engineer to design and deploy GenAI applications using Python, LLMs, and FastAPI in a scalable backend. You will build agent-based AI systems with LangGraph, develop RAG pipelines, and integrate with Azure, Snowflake, MongoDB, and other data stores while optimizing prompts and model performance.

The role emphasizes ETL workflows, Databricks notebooks, Docker/Kubernetes deployments, and collaboration with data engineering and business teams.

Qualifications

• Experience designing GenAI solutions with LLMs and Python.
• Backend development with scalable API services.
• Hands-on with cloud platforms and data tooling.

Responsibilities

• Design, develop, and deploy GenAI apps using LLMs and Python.
• Build agent-based AI systems with LangGraph and similar frameworks.
• Develop RAG pipelines using embeddings, vector databases, and information extraction.
• Implement prompt engineering, LLM evaluation, and model optimization.
• Build scalable backend services and APIs with Python and FastAPI.
• Integrate AI apps with Azure cloud services and enterprise systems.
• Work with Snowflake, MongoDB, and other databases.
• Develop and manage ETL pipelines and large-scale data processing flows.
• Use Databricks notebooks and dashboards; orchestrate workflows and deployments.

Skills

GenAI

LLMs

Python

API design

Cloud services

DevOps

Tools

FastAPI

LangGraph

Docker

Kubernetes

Azure

Databricks

Snowflake

MongoDB

Job description

Machine Learning Technologies, LLC seeks a Senior AI Engineer to design and deploy GenAI applications using Python, LLMs, and FastAPI in a scalable backend. You will build agent-based AI systems with LangGraph, develop RAG pipelines, and integrate with Azure, Snowflake, MongoDB, and other data stores while optimizing prompts and model performance.

The role emphasizes ETL workflows, Databricks notebooks, Docker/Kubernetes deployments, and collaboration with data engineering and business teams.