IC4 - Sr AI Engineer

Objective  

We’re building an AI-first organization - and we’re looking for engineers who actually build, not just experiment. We’re hiring a Senior AI Engineer to design, develop, and scale AI-powered products across our ecosystem - from LLM-based customer experiences to internal copilots that transform how teams operate. 

You’ll be part of a centralized AI Products Chapter, embedding into squads to bring real AI solutions into production. 

What you’lldo  

• Build and deploy Generative AI solutions (LLMs, RAG, agents, copilots). 

• Design and implement scalable AI systems in production. 

• Work across the full lifecycle: prototype → deploy → optimize. 

• Integrate models with real systems (APIs, data platforms, workflows). 

• Create evaluation frameworks, monitoring, and feedback loops. 

• Partner with Product, Data, and Engineering to ship real impact. 

What we’relooking for  

• Strong software engineering background (Python + modern stack). 

• Hands-on experience with LLMs / Generative AI / NLP. 

• Experience building and deploying ML/AI systems in production. 

• Familiarity with cloud (AWS/GCP), APIs, and data pipelines. 

• Strong problem-solving and execution mindset. 

Why this role  

• You won’t just build models → you’ll ship real AI products. 

• You’ll work across multiple squads → high leverage impact. 

• You’ll help define how AI Engineering is done at scale. 

Required Knowledge &Experience  

• Experience: 7+ years in Data & Tech, with at least 3 years specifically focused on AI/ML and hands-on experience in senior technical roles. 
  • Education: Bachelor’s or Master’s degree in Data Science, Computer Science, Applied Mathematics, or a related field. 
  • AI Expertise: Deep understanding of Deep Learning, Natural Language Processing (NLP), and Gen AI (LLMs, RAGs, Agents, etc.). 
  • Technical Stack: Proficiency in Python (PyTorch, TensorFlow) and experience with AI development frameworks (LangChain, LlamaIndex, Hugging Face). 
  • Cloud & Big Data: Experience in cloud-native AI services (AWS SageMaker or Google Vertex AI) and big data frameworks. 
  • Infrastructure: Familiarity with vector databases (Pinecone, Milvus, Weaviate) and MLOps tools for model tracking. 
  • Communication: Ability to explain complex AI concepts to non-technical stakeholders in a business-centric way. 
  • Ethics & Compliance: Knowledge of AI governance, including data privacy and model interpretability. 
  • Soft Skills: Proactive problem-solving, strategic thinking, and commitment to high-quality code and innovation. 

  If you’re excited about building real AI products (not just talking about them), we should talk.

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