Hire Pre-vetted AI Engineers

What is an AI Engineer?

An AI Engineer is a specialist who designs and builds artificial intelligence systems—leveraging machine learning, large language models, computer vision, NLP, and other AI techniques to create intelligent applications. AI Engineers do more than implement algorithms—they understand diverse AI domains, design solutions that solve real problems, integrate multiple AI technologies, and build systems that are reliable, ethical, and scalable. Whether you need someone to build AI-powered applications, architect AI strategies, or scale AI capabilities across your organization, a skilled AI Engineer brings broad AI expertise and systems thinking.

What makes AI Engineers valuable is their ability to understand the full AI landscape and know which tools fit specific problems. They bridge data science, machine learning, and software engineering. They build AI systems designed for real-world deployment, not just research. This is why forward-thinking organizations invest in AI Engineers. When you hire through Torc, you're getting someone who builds AI systems that create business value.

Technology Stack

AI Frameworks & Platforms

• TensorFlow, PyTorch for deep learning

• Hugging Face for NLP & foundation models

• LLM APIs (OpenAI, Claude, Gemini)

• LangChain & LlamaIndex for AI applications

Machine Learning & Deep Learning

• Computer vision techniques & frameworks

• NLP & transformers

• Reinforcement learning

• Generative AI techniques

AI Operations & Deployment

• Model deployment & serving

• MLOps practices & tools

• AI infrastructure (Kubernetes, cloud platforms)

• Monitoring & evaluation frameworks

Data & Feature Engineering

• Data pipelines & processing

• Feature engineering & stores

• Vector databases & embeddings

• RAG & knowledge systems

AI Integration & Applications

• REST APIs for AI services

• Chatbots & conversational AI

• Autonomous systems

• Recommendation systems

Key Qualities to Look For on an AI Engineer

Broad AI Knowledge — They understand diverse AI domains: machine learning, deep learning, NLP, computer vision. They know which tools fit which problems.

Systems Thinking — They design end-to-end AI systems considering data, models, deployment, monitoring, and ethics. They think about systems holistically.

Problem Formulation — They translate business problems into AI challenges. They know when AI is the right solution versus when simpler approaches suffice.

Innovation & Creativity — They explore novel approaches to problems. They stay current with emerging AI techniques and know when to experiment versus when to use proven approaches.

Pragmatism — They balance innovation with practicality. They deliver working solutions, not just research experiments.

Ethics & Responsibility — They think about fairness, bias, and responsible AI. They design systems that create value while minimizing potential harms.

Project Types Your AI Engineers Handle

AI Application Development — Building AI-powered applications and features. Real scenarios: AI-powered chatbots, recommendation systems, content generation tools, automation systems.

Computer Vision Systems — Building systems that understand images. Real scenarios: Image classification, object detection, document analysis, quality control.

Natural Language Processing — Building NLP systems. Real scenarios: Sentiment analysis, text classification, chatbots, information extraction.

Generative AI Integration — Integrating LLMs into applications. Real scenarios: Content generation systems, code generation tools, virtual assistants, knowledge systems.

AI Strategy & Architecture — Designing AI strategies and architectures. Real scenarios: AI roadmap development, technology selection, capability assessment.

Data Engineering for AI — Building data infrastructure for AI. Real scenarios: Data pipeline development, feature engineering, vector database setup.

AI Ethics & Governance — Ensuring responsible AI practices. Real scenarios: Bias detection & mitigation, fairness assessment, responsible AI frameworks.

Interview questions

Question 1: "Tell me about an AI-powered application or feature you built. What AI techniques did you use, what were the challenges, and what was the impact?"

Why this matters: Tests ability to select and integrate appropriate AI techniques for real problems. Reveals breadth of AI knowledge and practical implementation skills. Shows business impact thinking.

Question 2: "Describe a situation where AI didn't work for a problem you thought it would solve. What did you learn and how did you pivot?"

Why this matters: Tests realistic understanding of AI limitations and flexibility. Reveals whether they're overly optimistic about AI capabilities. Shows judgment about when AI is and isn't appropriate.

Question 3: "Walk me through how you'd approach building an AI system that handles sensitive data responsibly. What ethical and governance considerations would you address?"

Why this matters: Tests responsible AI thinking and governance awareness. Reveals whether they consider bias, fairness, transparency. Shows maturity about AI's societal impact.