What You'll Do:


Build and deploy agentic AI solutions that enable autonomous task completion across business functions
Develop reusable AI libraries and SDKs that accelerate product discovery and empower engineering teams
Build specialized AI tools that transform manual workflows into efficient automated processes
Design and implement smart internationalization libraries that enhance global user experiences
Create and optimize RAG systems that make company knowledge instantly accessible
Develop scalable orchestration frameworks for AI agents to handle complex business processes
Engineer prompt management systems and versioning tools for consistent AI interactions
Build and maintain AI-powered knowledge graphs to improve information discovery and utilisation
Create comprehensive LLM evaluation frameworks to ensure quality, accuracy, and performance
Research, evaluate, and integrate third-party AI tools and SaaS solutions into the business
Develop AI-powered analytics pipelines that transform unstructured data into actionable insights
Implement conversational AI interfaces that streamline internal support and enhance productivity
Stay current with advancements in GenAI, vector databases, and embeddings technologies
Contribute to our internal AI community through knowledge sharing and best practices
Work with cloud AI services to ensure scalability, reliability, and cost-effectiveness


What You Have:


Software engineering fundamentals
At least one year experience with Python and relevant AI/ML frameworks and libraries
Basic understanding of cloud platforms (GCP preferred, AWS/Azure acceptable)
Experience working with at least one AI provider API (OpenAI, Anthropic, or Google Gemini)
Hands-on knowledge of LLM orchestration frameworks like LangChain and LangGraph
Basic familiarity with LLM orchestration frameworks like LangChain or LangGraph
Basic understanding of AI agents and automation concepts
Understanding of vector databases and embedding models for semantic search
Basic experience with pre-trained models and model deployment concepts
Knowledge of RAG architectures and implementation patterns
Ability to implement prompt engineering best practices for LLM applications
Exposure to knowledge base and data indexing concepts (LlamaIndex familiarity is a plus)
Strong problem-solving skills and attention to detail
Excellent communication skills and ability to explain complex concepts to non-technical stakeholders
Demonstrated hunger to learn, innovate, and stay current with the rapidly evolving AI landscape
Ability to work collaboratively in a fast-paced environment