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’ll do
• 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’re looking 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 businesscentric 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.