Collaborate with product and software teams to integrate AI solutions and forward-deploy production-grade systems into enterprise applications 

Drive proof-of-concept initiatives and publish insights on emerging techniques 

Orchestrate LLM-based agentic systems 

Contribute in full-cycle AI development, from data pipelines and model training to deployment and monitoring 

Design, build, and optimize production-grade machine learning models 

Implement robust MLOps practices using Kubernetes, MLflow, and cloud services (AWS, Azure, GCP) 

Advocate for ethical AI principles, ensuring fairness, transparency, and security 

BSc/MSc/PhD in Computer Science or related field 

Proficiency in Python, ML frameworks (PyTorch, TensorFlow), and transformer-based architectures 

Proven experience in fine-tuning generative models (e.g. open-source LLMs, diffusion models) 

Experience deploying AI solutions using containerization (Docker, Kubernetes) and CI/CD pipelines. 

Proficiency in at least one agentic orchestration framework for building LLM-based systems 

Comfortable working in AI-first development environments, leveraging agentic tools (e.g., Claude Code, Codex) to plan, build, and iterate faster. 

Contributions to open-source AI projects 

Expertise in LLM evaluation, and performance-cost optimization 

Experience with advanced AI domains (graph neural networks, reinforcement learning) 

Active engagement in AI communities (Kaggle, Huggingface). 

Experience with agentic orchestration frameworks such as LangGraph, Google ADK, or similar tools