Mohammad Wasil Saleem's Portfolio

Mohammad Wasil Saleem

I'm a Machine Learning Engineer

Professional Summary

Machine Learning Research Engineer with 3.5 years of experience specializing in Computer Vision, NLP, and Generative AI. My work involves dedicated research in Deep Learning, GenAI, Multimodal AI, and Reinforcement Learning, coupled with architecting production-scale MLOps workflows. My portfolio further explores the development of Games and 3D modeling.

Work Experience

Machine Learning Software Engineer

April 2024 - Present Siemens Mobility Berlin, Germany

Siemens AI for Engineering : Project focused on the development and integration of AI solutions to automate domain-specific engineering workflows and significantly boost efficiency.
Artificial intelligence: transforming mobility for everyone: Company AI page
Designed and implemented Quality Agent, a company-wide tool for requirements analysis.
Developed a scalable multi-AI agent framework for automated test case generation for train braking systems.
Contributed to a project that won the Siemens Mobility Innovation Award, 2025.
Deployed across 7 distinct use cases, analyzing around 20 requirements per day.
Developed a self-service portal in JavaScript (GUI) and SQLite cache to improve adoption.
Integrated seamlessly with requirement management systems by developing secure APIs.
Engineered an automated Table Extraction pipeline (OCR) utilizing cloud-based AI solutions.
Designed to efficiently process highly unstructured, scanned technical documents.
Tech Stack: Python (LangGraph, Pydantic), JavaScript, SQL, Docker, Linux, AWS (IAM, S3, Bedrock, Textract), Azure (OpenAI, Document Intelligence), Git, CI/CD, Poetry, mypy, pre-commit.

Data Analyst - Research Assistant

February 2021 - March 2022 Leibniz-Institut für Agrartechnik und Bioökonomie e.V. (ATB) Potsdam, Germany

ATB Animal Welfare : Conducted statistical analysis of air exchange rates in naturally ventilated barns using methods such as ANOVA and model selection (AIC/BIC).
Individualized Livestock Production: Company page
Enhanced regression model performance by increasing R² from 0.65 to 0.85 through targeted feature transformation and optimization.
Developed scripts for task automation with Python and R; documented results in RMarkdown.
Tech Stack: R, Python, Excel, RMarkdown; Statistical and Probabilistic Modeling.

Machine Learning Research Engineer - Computer Vision (Autonomous Trains)

March 2022 - February 2024 Siemens Mobility Berlin, Germany

Siemens Driverless Train : Project focused on Assistance and driverless train operations to maximize system capacity, improve safety, and ensure sustainability.
Assisted and driverless train operation: Company page
Developed and fine-tuned an end-to-end pedestrian detection model for autonomous trains using panoptic segmentation, aligning custom dataset with COCO dataset format.
Designed and implemented an Active Learning framework (uncertainty and diversity-based query strategies), boosting model accuracy by 89.94% across five cycles using just 7% of queried data, supporting ADAS safety.
Self-supervised Learning: Improved clustering of unlabeled image data by fine-tuning embeddings of ResNet-152 and VGG16 pre-trained with ImageNet dataset using contrastive learning and visualizing the results using t-SNE.
Built a semi-automated Data annotation pipeline using CVAT and models such as YOLO, SAM, MaskDINO, Mask2Former, and PanopticFCN, reducing the annotation time by 76%.
Leveraged Multimodal foundation models (BLIP and BLIP-2) for zero-shot transfer and semantic image retrieval to streamline the data curation process across large visual datasets.
Tech Stack: Python, PyTorch, Detectron2, CVAT, Git, Linux (WSL2).

Unity3D Developer Intern

May 2018 - July 2018 ALG Media Private Limited New Delhi, India

Designed and developed the core mechanics and user interface for the Android 3D game.
Implemented dynamic runtime 3D mesh generation for tunnels, significantly boosting in-game performance.
Tech Stack: Unity3D, C#, JavaScript, Blender.

Education

Master of Science: Data Science

2019 - 2024 Universität Potsdam Potsdam, Germany

Grade: 1.8 GPA
Master's Thesis: 1.1 GPA - "Cost-Efficient and Model-Guided Multi-Query Strategy for Pedestrian Segmentation for ADAS in Railways"
In collaboration with Siemens Mobility
Thesis Link: Research Gate Link for Master Thesis
Proposal: Proposed an Active Learning-based Panoptic Segmentation method for railway safety, achieving an 89.94% AP improvement using only 7% of data points, while reducing labeling time by 76.19% through a deep learning–assisted human-in-the-loop annotation process.

Bachelor of Technology: Computer Science and Engineering

2015 - 2019 Jamia Hamdard New Delhi, India

Grade: 1.6 GPA
Project: "Visual Question Answering"

Skills

Languages

English (Fluent)

Deutsch (A2 CEFR level)

Hindi (Native)

Programming Languages

Python

JavaScript

SQL

Bash Script

Machine / Deep Learning Expertise / LLM Fine-Tuning

Neural Networks

CNNs

RNNs

Encoder-Decoder

Siamese Neural Networks

GANs

LLMs/SLMs

Transformers

RAG / AI Agents

SFT / RLHF

PPO / DPO

PEFT / LoRA / QLoRA

Multimodal Models

Computer Vision

NLP

Generative AI

Python Libraries & Frameworks

PyTorch

LangChain

LangGraph

OpenCV

Hugging Face

NLTK

scikit-learn

MLflow

WandB

Pandas

NumPy

Matplotlib

pytest

Pydantic

MLOps / DevOps

Git

Linux

Docker

GitLab CI/CD

Snowflake

VectorDB (ChromaDB)

GitHub Actions

Cloud

AWS IAM

AWS S3

AWS SQS

AWS SNS

AWS Bedrock

AWS Textract

Azure OpenAI

Azure Document Intelligence

Portfolio

A comprehensive portfolio featuring Machine Learning and Deep Learning research, Reinforcement Learning and GenAI, supported by Docker-based engineering and Game Dev projects.

All
Machine/Deep Learning
Computer Vision
Natural Language Processing
GenAI
Reinforcement Learning
Game Development

Pedetrians Detection for Trains

Proposed an Active Learning-based Panoptic Segmentation method for railway safety, achieving an 89.94% AP improvement using only 7% of data points, while reducing labeling time by 76.19% through a deep learning–assisted human-in-the-loop annotation process.

Neural Machine Translation with Attention

Implemented a Spanish-to-English Neural Machine Translation model with an Attention mechanism (from scratch), achieved a BLEU score of 25.37, comparable to the state-of-the-art.

Self-Driving Car

Trained a Deep Learning-based autonomous driving model using CNNs for steering prediction and road line detection on Synthetic Data generated via Unity3D simulation, with real-time image streaming over TCP/IP.

Blender Node-CoPilot (WiP)

Developed an Open source 3D Blender add-on that translates natural language into Shader and Geometry nodes (text-to-code) by fine-tuning a Small Language Model (SLM) using RLHF with PPO and integrating it with Blender’s API.

Conditional GANs - Generate New Faces

Generated realistic faces from CelebA dataset using conditional GANs, optimizing attribute selection for improved results.

Predicting User Affiliation of YouTube Commenters with Hierarchical Attention

Built a large-scale dataset of 10 million YouTube comments and classified user affiliation using a Hierarchical Attention Network trained on 20K labeled samples, achieving 89.69% accuracy.

VQA: Visual Question Answering (Vision + Language)

Developed a multimodal (vision-language) model trained on the COCO dataset, achieving 51.28% accuracy on the Visual Question Answering (VQA) challenge.

Ticker Checker

Developed a full-stack, Docker-containerized intelligent support system that uses Deep Learning for ticket classification and integrates an optimized RAG pipeline with a Re-Ranking Algorithm to retrieve highly relevant historical tickets.

Q-learning vs SARSA applied to Smart Cab

Evaluated the efficacy of two Reinforcement Learning algorithms, Q-learning and SARSA, in the context of the Smart Cab game.

Take‘em Out (2018)

Developed an FPS Android game in Unity3D, featuring enemy AI, player health systems, and progressive level design for enhanced gameplay difficulty.

Contact

Reach out to me!

Email

wasilmohd1@gmail.com

Location

Potsdam, Germany