Anjaneya (Reddy) Marimireddygari-AI & ML Engineer · LLM Systems · Generative AI · Python · IEEE Published
Check rate
Experience
Machine Learning Engineer Intern
Slash Mark
- Built and fine-tuned CNN and RNN architectures using transfer learning for real-world classification tasks — core deep learning skills applicable to BMW's multimodal LLM and GenAI vehicle function development.
- Implemented Dropout, Batch Normalisation, and Early Stopping across deep learning experiments; evaluated rigorously using precision, recall, F1-score, and confusion matrices for production-grade reliability.
- Developed an AI-powered attendance management system using LBPH facial recognition, deployed via Flask web interface with real-time SMS notifications — demonstrating end-to-end AI product delivery for real users.
- Collaborated across cross-functional teams to deliver scalable, documented ML pipelines designed for reproducibility — matching BMW's interdisciplinary team and research environment.
- Integrated AI tooling directly into the development workflow from design through to testing, maintaining high velocity without compromising correctness.
Data Science Engineer Intern
YBI Foundation
- Developed predictive ML models (Linear Regression, Decision Trees, ensemble methods) on structured business datasets, achieving up to 85% test accuracy — transferable to BMW's personalised context modelling for LLM-driven assistants.
- Preprocessed and feature-engineered datasets exceeding 5 GB using Pandas and NumPy, surfacing patterns that informed cost optimisation strategies — directly relevant to BMW's heterogeneous data format and SQL/GraphQL querying requirements.
- Built Tableau dashboards to visualise KPIs, sales trends, and customer behaviour, enabling data-driven decisions for non-technical stakeholders — matching BMW's requirement for excellent cross-team communication.
- Conducted in-depth exploratory data analysis (EDA) to surface trends, outliers, and correlations, then delivered actionable business recommendations bridging raw data and strategy.
Machine Learning Engineer Intern
Pantech Solutions (APSSDC)
- Designed and deployed FastAPI endpoints for real-time AI model inference — full ownership from development through to production, directly analogous to BMW's LLM API deployment requirements.
- Achieved 92% recall on highly imbalanced datasets using SMOTE oversampling, demonstrating rigorous ML evaluation skills required for BMW's AI memory and context engineering systems.
- Reduced cloud training pipeline costs by 35% through targeted optimisation of preprocessing and training workflows on Azure — aligning with BMW's Azure Pub/Sub and cloud integration stack.
- Executed complete end-to-end ML workflows: data ingestion, feature engineering, model training, evaluation, and deployment — the same lifecycle applied in BMW's LLM-driven assistant development.
- Benchmarked deployed models under production conditions; identified performance bottlenecks and refactored pipelines for reliability and scale.
AI-Powered Attendance System via Facial Recognition — IEEE ICCSP 2024 (Published)
- Designed and deployed a full end-to-end deep learning pipeline using LBPH face recognition, Flask web interface, SQLite database, and Twilio SMS API — a complete AI product serving real institutional users.
- Achieved seamless Human-AI interaction through a live camera feed interface, directly aligned with BMW's goal of enhancing in-vehicle Human-AI interaction via multimodal assistants.
- Published at IEEE Xplore — demonstrating ability to contribute innovative research ideas in a structured R&D environment, consistent with BMW's research internship culture.
Big Mart Sales Forecasting System — Retail ML Pipeline
- Built an end-to-end ML sales forecasting pipeline using ensemble methods (Random Forest, Gradient Boosting), feature engineering, and Tableau dashboards to deliver actionable inventory insights.
- Applied the same data lifecycle — ingestion, EDA, feature engineering, model training, evaluation — used in BMW's LLM memory management and context engineering workflows.
Industry experience
See where this freelancer has spent most of their professional time.
Experienced in Information Technology.
Business area experience
See which departments and functions this freelancer has contributed to most.
Experienced in Information Technology, Product Development, and Research and Development.
Summary
AI software engineer and M.S. Computer Science student at Bauhaus-Universität Weimar with production experience building end-to-end ML and deep learning systems. Skilled in Python, LLM integration, FastAPI, cloud deployment (Azure), and multimodal AI pipelines. Published IEEE researcher in deep learning-based AI systems. Bringing a first-principles engineering mindset, real deployment experience, and a strong drive for impactful AI innovation.
Skills
- Ai & Llm: Large Language Models (Llms), Multimodal Llms, Prompt Engineering, Langchain, Generative Ai, Agentic Systems, Rag Pipelines
- Ml & Deep Learning: Tensorflow, Pytorch (Familiar), Scikit-Learn, Cnn, Rnn, Transfer Learning, Lbph, Smote, Model Evaluation (F1, Precision, Recall)
- Languages: Python (Strong), Java, Kotlin (Familiar), Sql, Graphql (Familiar), Html, Javascript
- Cloud & Devops: Microsoft Azure (Certified), Azure Pub/Sub, Aws (Familiar), Docker, Ci/Cd, Git, Github
- Apis & Frameworks: Fastapi, Flask, Rest Apis, Pydantic, Android Automotive (Familiar), Microservices
- Data & Tools: Pandas, Numpy, Tableau, Eda, Feature Engineering, Jupyter Notebook, Vs Code
Languages
Education
Bauhaus-Universität Weimar
M.S. · Computer Science for Digital Media · Germany
MITS, JNTUA
B.Tech. · Computer Science & Engineering · India
Certifications & licenses
Claude Code in Action
Google Professional Certificate: Data Analytics
Coursera
Statistics
Experience
Expertise
Qualifications
Profile
Frequently asked questions
Have questions? Find more information here.
Anjaneya is based in Weimar, Germany.
Anjaneya speaks the following languages: English (Advanced), German (Elementary).
Anjaneya has at least 1 year of experience. During this time, Anjaneya has worked in at least 2 different roles and for 3 different companies. The average length of individual experience is 2 months. Note that Anjaneya may not have shared all experience and actually has more experience.
Based on recent experience, Anjaneya would be well-suited for roles such as: Machine Learning Engineer Intern, Data Science Engineer Intern, AI-Powered Attendance System via Facial Recognition — IEEE ICCSP 2024 (Published).
Anjaneya's most recent position is Machine Learning Engineer Intern at Slash Mark.
In recent years, Anjaneya has worked for Slash Mark, YBI Foundation, and Pantech Solutions (APSSDC).
Anjaneya is most experienced in industries like Information Technology.
Anjaneya is most experienced in business areas like Information Technology, Research and Development, and Product Development. Anjaneya also has some experience in Business Intelligence.
Anjaneya holds a Master in Computer Science for Digital Media from Bauhaus-Universität Weimar and a Bachelor in Computer Science & Engineering from MITS, JNTUA.
Anjaneya has 3 certificates. These include: Microsoft Certified: Azure Data Engineer Associate, Claude Code in Action, and Google Professional Certificate: Data Analytics.
Anjaneya is immediately available full-time for suitable projects.
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Calculated based on our freelancers’ daily rates as of 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
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