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.

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