Data Scientist - Gen AI ML - Tampa/Irving / Mississauga
At a glance
Mid-level AI Research role at Fa Ertb Saasfaprod1. United States · $48,000–$170,000 base.
aimlroles summary, based on the employer's posting.
What you'll do
- Build multi-agent workflows with LangGraph, then develop retrieval systems using LlamaIndex and vector databases
- Choose and connect commercial or open-source language models according to their cost and performance
- Create scalable backend services in FastAPI with asynchronous Python, then package AI services in Docker for production
- Fine-tune language models with PyTorch or TensorFlow and improve latency, cost, reliability, and domain performance
- Use prompt design and monitoring tools to improve GenAI workflows in production
What you bring
- At least five years building and launching GenAI applications for production use
- Strong Python skills and hands-on use of modern AI libraries such as LangChain or LlamaIndex
- Experience with vector search, embedding models, and advanced retrieval methods
- Knowledge of model fine-tuning and local LLM quantization or hosting
- Familiarity with production monitoring, API security, and CI/CD for machine learning
Who this fits
This role suits a mid-level practitioner who combines machine learning work with production software development. It is not available to independent contractors.
From the employer
Role Summary:
We are seeking a Generative AI Engineer to build, optimize, and scale production-ready AI applications. You will design complex multi-agent systems, implement advanced RAG pipelines, and manage the deployment of both frontier and local LLMs. The ideal candidate blends deep machine learning expertise with modern software engineering practices.
Technical Stack:
LLMs: Gemini, OpenAI, Claude, Llama, and Local Model deployment.
Frameworks: LangChain, LlamaIndex, and Hugging Face.
Orchestration: LangGraph and Multi-Agent Systems (MAS).
Development: Python, FastAPI, and Asynchronous Programming.
RAG & Data: PostgreSQL, Vector Databases, and Advanced Retrieval strategies.
ML/DL: PyTorch, TensorFlow, and Model Fine-tuning.
Deployment: Docker, Production API management, and LLM monitoring.
Tools: Prompt Engineering, Workflow Design, and GenAI Optimization.
Key Responsibilities:
Develop and orchestrate sophisticated AI workflows using LangGraph and multi-agent architectures.
Build and maintain Advanced RAG systems utilizing LlamaIndex and vector databases for high-accuracy retrieval.
Integrate and swap diverse LLMs (commercial and open-source) based on performance and cost requirements.
Design and deploy high-performance, scalable backend services using FastAPI and Async Python.
Fine-tune large language models (LLMs) using PyTorch/TensorFlow to improve domain-specific performance.
Optimize GenAI workflows for latency, cost, and reliability using advanced prompt engineering and monitoring tools.
Containerize and deploy AI services via Docker to production environments.
Required Qualifications:
5 years of hands-on experience building and deploying GenAI applications in a production setting.
Strong proficiency in Python and the modern AI library ecosystem (LangChain, LlamaIndex, etc.).
Experience with vector search, embedding models, and advanced data retrieval patterns.
Knowledge of model fine-tuning techniques and local LLM quantization/hosting.
Familiarity with production-grade monitoring, API security, and CI/CD for ML.
Compensation, Benefits and Duration
Minimum Compensation: USD 48,000
Maximum Compensation: USD 170,000
Compensation is based on actual experience and qualifications of the candidate. The above is a reasonable and a good faith estimate for the role.
Medical, vision, and dental benefits, 401k retirement plan, variable pay/incentives, paid time off, and paid holidays are available for full time employees.
This position is not available for independent contractors
No applications will be considered if received more than 120 days after the date of this post