AI Engineer

AI Engineer (Full-Stack AI Solutions)

Join Dry Ground AI to design, develop, deploy, and optimize practical AI and agentic automation solutions for clients.

Role Overview

You’ll combine AI/ML engineering (including generative AI and model fine-tuning) with automation and agent-based system integrations to enhance client operations and streamline workflows.

Responsibilities

AI/ML Development

• Design, build, and deploy machine learning and generative AI models for custom use cases
• Fine-tune and optimize LLMs (e.g., GPT, BERT) using Hugging Face Transformers
• Build data pipelines for preprocessing, feature engineering, and training
• Test, validate, and monitor model performance in production-like scenarios; iterate for reliability and accuracy
• Collaborate cross-functionally to embed AI into products, platforms, and services
• Mentor junior engineers and contribute to technical leadership

Automation & Agentic Systems

• Build and maintain automation workflows using tools including LangChain, LangGraph, LangSmith, LangFuse, n8n, and ElevenLabs (plus CMS integrations)
• Implement system-to-system integrations using APIs for intelligent process automation
• Apply prompt and context engineering; implement prompt management and QA for agents/assistants
• Explore Microsoft Copilot Studio, PowerApps, and PowerAutomate (preferred but not required)
• Evaluate emerging AI/automation tools and match them to client use cases
• Scope, deliver, and maintain agentic and automation solutions aligned with business goals

Requirements

• 2+ years hands-on AI/ML experience, including training and deploying models in production
• Strong Python skills; experience with TensorFlow, PyTorch, Scikit-learn, and Hugging Face Transformers
• Experience with AI tooling such as LangChain, OpenAI, or Stable Diffusion
• Practical experience with automation platforms and AI agent builders (listed above)
• Deep understanding of ML concepts, neural networks, and NLP
• Proficiency with REST APIs for tool/platform integrations
• Familiarity with cloud-based AI/ML platforms
• Ability to translate business problems into technical AI/automation solutions

Preferred

• Experience with agentic AI frameworks, voice automation, and prompt tuning systems
• Exposure to MLOps best practices (model versioning, CI/CD, monitoring)
• Knowledge of reinforcement learning, GANs, or edge AI deployments
• Familiarity with distributed computing (e.g., Spark or Ray)
• Background in AI agency workflows and client-facing delivery