Senior Data Engineer / AI Engineer

Quick Facts

• Role: Senior Data Engineer / AI Engineer

• Work style: Remote

• Contract length: 12-month contract

Description

Build and deploy AI agents and machine learning models, with a strong focus on GenAI and agentic workflows. Design intelligent agents leveraging large language models (LLMs), planning algorithms, and decision-making frameworks, then integrate secure, scalable solutions into applications. Continuously evaluate and improve agent performance using feedback mechanisms, reinforcement learning, and safety/guardrail mechanisms.

Responsibilities

• Develop and deploy AI agents and machine learning models using frameworks such as TensorFlow or PyTorch

• Build and experiment with GenAI models and agentic workflows

• Use Azure cloud technologies, including Azure Data and AI platforms (Databricks, Fabric, AI Foundry)

• Design intelligent AI agents using LLMs, planning algorithms, and decision-making frameworks

• Build secure, scalable agents and integrate them into applications/workflows for cross-platform deployment

• Advance agent capabilities via research and performance evaluation, including safety and guardrail mechanisms

• Optimize agent performance using feedback mechanisms, reinforcement learning, and user interaction analysis

• Collaborate across the stack (backend services, model pipelines, and frontend interfaces)

• Prototype rapidly, iterate on ideas, and contribute in a startup-style environment

• Work with senior engineers and researchers to deliver production-grade AI systems

Requirements

• Proficiency in Python and other relevant programming languages

• Deep expertise in cloud technologies, focused on Microsoft Azure

• Experience with Azure Data and AI platforms (Databricks, Fabric, AI Foundry)

• Experience building and deploying AI agents and machine learning/GenAI models

• Ability to design agents using LLMs, planning algorithms, and decision-making frameworks

• Experience integrating AI agents into applications and workflows

• Knowledge of safety/guardrail mechanisms and performance evaluation

• Familiarity with feedback mechanisms and reinforcement learning for optimization