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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