About Me

I have a MS degree in Scientific Computing at Heidelberg University, Germany and posses 4 years of experience in ML and software development. I have previously worked in Nuremberg, Germany as a Machine Learning Engineer where I worked extensively in agile scrum machine learning engineering development and mentoring junior engineers for agile processes, code reviews, and organizing KT sessions. I’m currently seeking for ML and data science full-time opportunities across EMEA to start from Q1 2023.

I pursued my MSc thesis at SAP AI in Berlin where I worked on problems related to neural source code generation from natural language and code completion. Prior to joining SAP AI Foundations in Berlin, I worked for one and half years at SAP as Data Scientist & Developer in Walldorf, Germany since June 2019 where I was involved in agile software development (Python, Node.js, Jenkins CI/CD, Kubernetes, Docker, SonarQube)and delivering end-to-end ML (Pandas, Numpy, Seaborn, Tensorflow) PoC to customers for commodity code prediction, master data record categorisation, and automatic labelling of web documents using doc2vec and clustering. Prior to coming to Germany, I worked as Research Scholar at IIT Kanpur in India for 1 year.

Being industrious, determined, smart and passionate, I am always excited to take on new challenges and pursue new learning opportunities.

Education

• Master of Science in Scientific Computing, University of Heidelberg, Germany, 2021

Work Experience

• Machine Learning Engineer, April 2022 - Present
  • GFK SE, Nuremberg
  • Deployed a monitoring and alerting system for gfkNewron with Grafana dashboard, custom metrics recording in Prometheus, and Slack integration and email notifications to achieve better microservice monitoring, data consistency, and KPI tracking.
  • Mentored junior engineers for agile process, code reviews, organizing KT sessions, and participated in interviewing.
• Data Scientist, April 2020 - May 2021
  • SAP SE, Germany (Berlin, Walldorf)
  • Fine-tuned and deployed hybrid Seq2Seq-BART model on GCP, using data augmentation, pre-training techniques for natural language-source code system.
  • Pretrained GPT-2 and RoBERTa LLM on Python code from scratch for developing RESTful web service for code auto-completion and NL2Code system.
  • Delivered an end-to-end ML PoC to business customers for automating master data categorization, sales order approval, and commodity code prediction
  • Implemented & deployed SonarQube Git Pull Request decorator in Jenkins CI/CD pipeline for static code analysis
  • Responsible for python development, improvements, bug fixing for microservice ML offering, Data Attribute Recommendation.
• Software Developer, June 2019 - March 2020
  • SAP SE, Walldorf, Germany
  • Developed clustering & embedding techniques for content classification.
  • Improved spelling correction service by integrating semantic similarity.
  • Developed lightQueue feature for crawler in NodeJS to reduce 40% RAM usage.
• Research Scholar, August 2017 - July 2018
  • Indian Institute of Technology Kanpur, India
  • Developed a neural multilingual algorithm setup to retrieve documents in language x using queries in language y in absence of aligned/parallel corpus.
  • Worked on automatic query generation for resource-starved Indian languages in absence of aligned corpus by incorporating multilingual topic modeling.
  • Supervisor: Prof. Arnab Bhattacharya

Technical Skills

• Programming: Python, Java, Bash, SQL, HTML, and LATEX

• Dev Tools: Jenkins CI/CD, Gitlab, Docker, Kubernetes, GCP, pgAdmin, Postman, REST, Scrum, Agile, Kanban

• ML Stack: PyTorch, Tensorflow, HuggingFace, Sklearn, Pandas, NumPy, FastAI, spaCy, NLTK, Gensim, VertexAI, Jupyter, LangChain, MLflow Airflow, Streamlit, SHAP, ONNX, DeepSpeed

Language Skills

• Languages: English (Fully Proficient), German (B1.1), Bengali(Native), and Hindi (Advanced)

Strengths

1. Proficient in breaking down business problems into machine learning solutions using ML stack (Pytorch, Scikit-learn, Tensorflow, HuggingFace, MLFlow).

2. Python backend development (deploying new features on SaaS, migrating codebase from Tensorflow v1.0 to v2.0, etc.) and DevOps (Kubernetes, GCP, Docker, Jenkins CI/CD).
3. Cross collaboration in agile teams with stakeholders and acted as a bridge and managed a smooth workflow between research and engineering teams, and ability to organise KT sessions on machine learning and software engineering principles across the company.