Role overview
- Master's degree in Computer Science, Data Science, or a similar technical field
- 3+ years of significant experience as a Python Developer or ML Engineer, with a recent focus on deploying solutions powered by Large Language Models (LLMs)
- Proven expertise in building production-ready AI agent workflows, orchestration layers, or platforms using Python frameworks (e.g., Langchain, LangGraph, or AutoGen)
- Mastery of Python for enterprise-level development, strong knowledge of core software development principles, and experience integrating AIOps/MLOps best practices (unit tests, CI/CD, Git)
- Practical experience with modern cloud platform technologies (VertexAI, Kubernetes or equivalents) for deploying scalable GenAI services
- Strong versatility and a demonstrated willingness to work across the full stack—from backend agent logic to Vector DB and RAG infrastructure
- Entrepreneurial mindset, high autonomy, and the ability to turn ambiguous, high-level business goals into concrete, efficient GenAI features
- Fluent technical English (written and verbal).
What you'll work on
- Product-Focused AI Agent Development
- Design, develop, and deploy goal-oriented AI agents and multimodal or conversational experiences (leveraging frameworks like Langchain, LangGraph, or AutoGen) to automate complex, high-impact workflows within our flagship product
- Own the industrialization and production lifecycle of deployed agents, establishing robust AIOps/AgentOps processes for monitoring performance, ensuring version control of agent blueprints, and guaranteeing production-grade reliability and low-latency response times
- Collaborate closely with Product Managers to translate complex business challenges into concrete, measurable GenAI solutions, focusing on maximizing the return on investment (ROI) of LLM and agent usage
Develop the specialized backend services and tool-calling APIs necessary for agents to interact securely and effectively with our existing enterprise systems and data sources
Core Agentic Platform Construction
Develop modular, reusable components for the internal Agentic Platform, including dynamic toolkits, sophisticated orchestration layers, and specialized evaluation harnesses for agent performance and safety
Implement and optimize the entire data infrastructure pipeline critical for GenAI, including managing Vector Databases, designing highly efficient RAG (Retrieval-Augmented Generation) pipelines, and establishing mechanisms for continuous Fine-Tuning
Champion and implement GenAI-native development standards, integrating best practices for AIOps, CI/CD, and documentation to ensure maximum code reusability and operational quality at scale
Lead the technical watch on the evolving LLM landscape (e.g., open-source vs. proprietary models) and spearhead the adoption of advanced techniques such as prompt engineering, multi-agent coordination, and complex agentic pattern design