About this role
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 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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