About the role
Objective We’re building an AI-first organization — and we’re looking for engineers who actually build, not just experiment. We’re hiring an AI Engineer to develop, integrate, and maintain 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) under standard production practices. • Implement scalable AI components and services within production systems. • Work across the full software lifecycle: assist in prototyping, deploy models, and optimize performance. • Integrate AI models with real-world systems, APIs, data platforms, and backend workflows. • Implement evaluation frameworks, monitoring mechanisms, and feedback loops to track model performance. • Partner with Product, Data, and Engineering squads to deliver impactful AI-driven features.
What we’re looking for • Solid software engineering background (Python + modern backend stack). • Hands-on experience building with LLMs, Generative AI, or NLP frameworks. • Practical experience deploying ML/AI features into production environments. • Familiarity with cloud services (AWS/GCP), API design, and data pipelines. • A strong problem-solving, execution-oriented, and continuous-learning mindset.
Why this role • You won’t just build models → you’ll ship real AI products into production. • You’ll collaborate across multiple squads → gain broad exposure and technical growth. • You’ll contribute directly to shaping best practices for AI Engineering within the organization.
Required Knowledge & Experience • Experience: 3–5 years of overall experience in Data & Tech, with at least 1.5–2 years of hands-on experience developing AI/ML and Generative AI solutions. • Education: Bachelor’s or Master’s degree in Data Science, Computer Science, Applied Mathematics, or a related technical field. • AI Competencies: Strong foundational understanding of Deep Learning, Natural Language Processing (NLP), and Generative AI (LLMs, RAG architecture, function calling, agents). • Technical Stack: Proficiency in Python and familiarity with ML libraries (PyTorch or TensorFlow), alongside hands-on use of AI frameworks (LangChain, LlamaIndex, or Hugging Face). • Cloud & Data Platforms: Hands-on experience with cloud-native AI/ML services (AWS SageMaker or Google Vertex AI) and standard data processing tools. • Infrastructure & Tools: Practical experience using vector databases (Pinecone, Milvus, Qdrant, or Weaviate) and MLOps tools for model tracking and logging. • Communication: Ability to clearly explain technical trade-offs and AI concepts to both technical teammates and product partners. • Ethics & Compliance: Basic understanding of AI safety, data privacy, and responsible AI practices. • Soft Skills: Proactive problem-solving, strong sense of ownership, willingness to learn, and commitment to writing clean, maintainable code.
If you’re excited about building real AI products (not just talking about them), we should talk. 🚀
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