5 Key Steps for Hiring the Right Generative AI Engineers for Your Business

Generative AI is transforming how businesses operate, automating content, powering voice assistants, and unlocking new ways to serve users. But real impact requires more than plugging into an API. It takes skilled engineers who understand models, data, and deployment.

If you’re looking to hire generative AI engineers, you need more than generic ML resumes. You need problem-solvers who can build real-world applications with speed, accuracy, and user empathy. This blog breaks down five essential steps to help you find, assess, and hire the right talent to bring your AI vision to life.

Step 1: Define What “Generative AI Engineer” Means for Your Business

Before launching your hiring process, start with clarity. The role of a generative AI engineer can mean different things to different companies. Some are focused on R&D and foundational model training. Others are seeking engineers who can fine-tune large language models (LLMs) for niche applications like voice AI or summarisation. Others still may need product engineers who can optimise inference, handle integrations, and ship AI-powered features to users.

Ask yourself:

• What problem are we solving with generative AI?
  
• Are we building something from scratch or integrating existing tools?
  
• Do we need deep research expertise or fast production deployment?
  
• Will the engineer be collaborating with product, design, or customer success?
  

For instance, if you’re developing a multilingual voice assistant, you’ll want to hire generative AI engineers with experience in text-to-speech (TTS), natural language understanding (NLU), and emotion detection. On the other hand, if you’re in fintech creating summarisation tools for reports, NLP and prompt engineering may be more relevant.

At Nurix, we’ve found that aligning role definition with business context is the first step to hiring talent that truly moves the needle. We don’t hire AI generalists, we hire engineers who can design and scale real-world, emotionally aware voice interactions.

Step 2: Identify the Technical Skill Set That Matches Your Use Case

Once the role is defined, it’s time to get technical. Generative AI is a broad field, and the stack can get deep quickly. If you want to hire generative AI engineers who deliver results, ensure they have hands-on experience with the tools and frameworks that matter to your use case.

Here’s a breakdown of relevant skill areas:

Core Technical Skills

• Machine Learning Frameworks: PyTorch, TensorFlow, JAX
  
• Model Training & Fine-Tuning: Familiarity with transformers (BERT, GPT, T5, etc.), Hugging Face libraries, LoRA, PEFT techniques
  
• Prompt Engineering & Retrieval-Augmented Generation (RAG): Building smarter prompts and leveraging vector databases like FAISS or Pinecone
  
• Deployment Skills: Docker, FastAPI, ONNX, TorchScript, serverless inference
  
• Cloud & DevOps: AWS (especially SageMaker, EC2 GPU), GCP, Kubernetes, Terraform
  

Domain-Specific Expertise

• Text-based generation: Summarisation, email writing, synthetic data creation
  
• Voice-based AI: TTS, ASR, emotion detection, latency-aware inference
  
• Multimodal AI: Combining image, video, or audio with text models
  

Soft skills also matter. Engineers should be comfortable experimenting, learning quickly, and communicating their findings with both technical and non-technical stakeholders.

At Nurix, the ideal hire doesn’t just build technically sound models, they deeply understand user needs and can design AI that engages with empathy and accuracy in real time.

Step 3: Craft a Job Description That Stands Out

Once you know who you need, make sure they know why they should join you. Top AI engineers aren’t just applying to open roles, they’re being recruited actively by startups, FAANG companies, and emerging AI labs. Your job description must cut through the noise.

What to Include in a Strong Job Description

• A compelling mission: Explain how your AI product improves lives or solves a unique problem.
  
• Specific role responsibilities: Example: “Fine-tune multilingual LLMs for voice-based interactions” or “Build synthetic customer dialogue generation pipelines.”
  
• Tech stack: Include your core tools and platforms.
  
• Growth opportunities: Make it clear how they’ll learn, grow, and lead.
  
• Your AI maturity level: Are you just starting out or scaling fast?
  
• Impact potential: Help candidates envision what they’ll build and how it will be used.
  

Example Snippet:

“At Nurix, we’re building voice-first AI that speaks like a human and listens like one too. We’re looking to hire generative AI engineers who can fine-tune cutting-edge models and deploy emotionally intelligent voice agents across industries like healthcare, banking, and retail.”

A clear, mission-driven JD helps attract candidates aligned with your goals, not just chasing a high paycheck.

Step 4: Design a Hiring Process That Filters for Real-World Skills

Hiring AI engineers isn’t just about assessing technical depth. You want people who can ship models, handle messy data, and build robust systems that scale. A great hiring process reflects that.

Recommended Hiring Flow

1. Screening Call (30 mins)
   
   • Gauge interest in your mission, communication skills, and ability to explain generative AI simply.
     
   • Ask about previous experience with LLMs or production deployments.
     
2. Technical Task or Take-Home Challenge (3–5 hrs)
   
   • Examples: “Fine-tune a model to classify user queries” or “Design a TTS pipeline using a pre-trained LLM.”
     
   • Focus on their approach, documentation, and trade-off decisions, not just code quality.
     
3. Deep Dive Interview (60–90 mins)
   
   • Go over the challenge. Ask them to explain why they made certain design choices.
     
   • Explore past work and edge cases they handled.
     
4. System Design & Culture Fit (60 mins)
   
   • Ask them to design an AI system (e.g., multilingual voice agent) and explain architecture, latency handling, fallback strategies.
     
   • Involve future teammates and gauge collaboration fit.
     

The goal is to hire generative AI engineers who can not only build models but understand latency trade-offs, ethical risks, and end-user behaviour.

Step 5: Offer More Than a Paycheck, Offer Purpose and Ownership

Top-tier generative AI engineers are drawn to mission, autonomy, and growth. Competitive compensation is necessary, but it won’t close the deal alone. To win great talent, create an environment where engineers can explore, lead, and see their work come to life.

What matters most:

• Ownership: Let them lead projects, not just execute tasks.
  
• Clear AI roadmap: Show them where your product is going and how they can shape it.
  
• Supportive infrastructure: Provide GPUs, cloud credits, tooling, and the right team structure.
  
• Learning culture: Encourage paper reading, attending conferences, and internal innovation days.
  

At Nurix, we give engineers the chance to work on the frontier of voice-first AI, pushing boundaries in multilingual TTS, emotional response modelling, and human-like conversation flow. That kind of ownership attracts creators, not just coders.

Bonus: Where to Find the Best Generative AI Engineers

To hire generative AI engineers, don’t just rely on traditional job boards. Some of the best talent lives in niche communities and open-source ecosystems.

Where to look:

• Hugging Face & GitHub: Check contributors to transformer models and voice AI projects.
  
• AI Twitter/X & LinkedIn: Look for thought leaders and hands-on engineers sharing papers and projects.
  
• ArXiv & OpenReview: Find engineers publishing in applied ML or NLP.
  
• Discord & Slack Communities: ML GDEs, AI Makerspace, and niche groups often have high-signal talent.
  
• Referrals: Ask existing engineers who they’d want to work with or learn from.
  

Being proactive with outreach and showcasing your AI mission will help you stand out in a noisy hiring market.

Conclusion

Your AI goals won’t come to life without the right team behind them. To truly innovate, you must hire generative AI engineers who bring deep expertise and the ability to turn complex models into business outcomes.

At Nurix, we help companies build high-performance AI teams, from model developers to real-time voice AI experts. Whether you’re scaling a product or launching a new AI initiative, we’re here to support you with the right people and platform.