Srikrishna Kumaran

Highly motivated and result-driven AI Engineer specializing in designing and deploying advanced AI and Generative AI (GenAI) solutions. Skilled in building intelligent, scalable, and adaptive systems using large language models (LLMs), multimodal reasoning, and autonomous agent architectures. Experienced in developing end-to-end AI pipelines, fine tuning, and real-time inference workflows. Looking forward to collaborate and contribute to deliver elegant, impactful, next-generation AI solutions.

AI Developer – Amsterdam (2025)

Built an Azure OpenAI‑powered medical triage system for Dutch GPs, integrating ABCDE assessment, symptom routing, and urgency classification.

Designed advanced prompt engineering for medical criteria, dynamic question generation, and severity scoring.

Delivered a secure, HIPAA‑compliant Streamlit web app with encrypted storage, user authentication, and conversation history management.

Ensured clinical accuracy through iterative feedback with healthcare professionals.

University Research Assistant – Vrije Universiteit Amsterdam (2023–2024)

Applied Bayesian networks and multi‑objective optimization to analyze IoT system architectures (DingNet).

Developed probabilistic models in Python to evaluate trade‑offs in reliability, energy efficiency, and adaptability.

Conducted risk assessments and generated Pareto‑optimal solutions for sustainable system design.

University Teaching Assistant – Vrije Universiteit Amsterdam (2023)

Mentored students on self‑adaptive systems, guiding assignments and architectural enhancements.

Supervised GitHub projects involving HTTP servers, robotic adaptation with ROS, and UPISAS extensions.

Coordinated team efforts, provided structured feedback, and promoted evidence‑based learning.

Category Procurement Intern – FrieslandCampina, Amersfoort (2022)

Engineered a centralized data pipeline with Azure Data Factory & SQL, improving procurement efficiency by 30%.

Built a cost estimation pipeline (77% accuracy) and an ML classification workflow (70% accuracy boost).

Deployed ML apps with Streamlit, integrated MLflow for versioning, and reduced manual processing by 40%.

Master’s in Computer Science – Software Engineering & Green IT Vrije Universiteit Amsterdam (2021–2024)

Specialized in Bayesian networks, optimization, and sustainable computing.

Conducted research on self‑adaptive IoT systems using probabilistic modeling and multi‑objective optimization.

Gained strong foundations in machine learning, deep learning, and MLOps, with hands‑on projects in LLMs, multimodal AI, and scalable data platforms.

Teaching Assistant and Research Assistant roles reinforced expertise in mentoring, architectural trade‑offs, and applied AI research.

Bachelor of Technology – Computer Science & Engineering SRM Institute of Science and Technology, Chennai (2016–2020)

Built a solid base in algorithms, programming, and data structures.

Early exposure to AI/ML concepts and software engineering practices.