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. 🚀