Recruiter-Optimized View
Designed to give you a complete picture in under 2 minutes
Mohith Doddapaneni
AI Engineer
AI Engineer with around two years of professional experience in AI projects, automation, and digital transformation at Yara International. Experienced in requirements gathering, feasibility studies, implementation, documentation, and stakeholder communication. Specialised in Generative AI, Agentic AI, LLM engineering, and process improvement. Currently pursuing an M.Sc. in Applied AI at Deggendorf Institute of Technology, Germany. Organised, responsible, and a strong team player with excellent English skills (C1).
M.Sc.
Applied AI for Digital Production Management
Deggendorf Institute of Technology
Mar 2025 — Present
B.E.
Electrical & Electronics Engineering
National Institute of Engineering
Jul 2019 — Jun 2023
GPA/Grade: 8.57 / 10.0 CGPA
Complete ML & NLP Bootcamp + MLOps
Udemy · Jun 2023
Power Platform Fundamentals (PL-900)
Microsoft · Apr 2023
Complete Python Bootcamp
Udemy · Sep 2022
Recognised for AI-Driven Solution with Measurable Impact
Yara International
Associate Engineer – AI & Data
Yara International
- ▸Deployed GPT-3.5 conversational knowledge platform on Azure across five business teams
- ▸Built CNN image analysis model achieving 80% accuracy — end-to-end from data to deployed output
- ▸Contributed to PoC development for AI use cases: scoping, feasibility, prototyping, stakeholder assessment
Intern – Process Automation
Yara International
- ▸Independently built Power Apps and Power Automate solutions for IT service management
- ▸Delivered requirements, build, test, and documentation in a structured, self-directed manner
Production-grade hybrid retrieval-augmented generation system
Hybrid retrieval combining dense vector search (ChromaDB) and sparse BM25 via Reciprocal Rank Fusion, with …
LangGraph-based autonomous multi-agent research pipeline
LangGraph StateGraph with five specialised agents (Planner, Researcher, Analyst, Writer, Critic) with condi…
QLoRA fine-tuning of Phi-2 on custom instruction dataset with streaming inference
QLoRA with 4-bit NF4 quantisation reducing VRAM from ~12 GB to ~6 GB, LoRA adapter matrices, TRL SFTTrainer…
Interested in working together?
I'm currently open to opportunities. Let's have a conversation.