Mar 2025 – Present · 1 yr 2 mos · Munich, Bavaria, Germany · On-site

• AI for the Automotive Industry - Working on a lot of interesting AI use cases, building AI and agentic systems

Oct 2024 – Feb 2025 · 4 mos · Munich, Bavaria, Germany · Hybrid

• As part of the Data Science Practical at LMU Munich with AT, I leveraged my skills as an AI Engineer to develop innovative proof-of-concept solutions aimed at optimizing planning and reporting within the insurance industry. This included the development and deployment of a RAG-based chatbot, enabling instant and accurate retrieval of information from intricate compliance documents. Moreover, I engineered an intelligent end-to-end assistant capable of providing comprehensive answers on compliance and software, and automating the creation of detailed compliance reports, effectively transforming a task that typically took days into a matter of minutes.

Mar 2024 – Mar 2025 · 1 yr · Munich, Bavaria, Germany · On-site

• As an AI Engineer, I specialized in leveraging Generative AI to automate and optimize electronics design. My work focused on developing AI-driven solutions that transformed functional requirements into detailed schematics and bills of materials, significantly accelerating the design process. Additionally, I contributed to streamlining data-driven workflows, reducing manual effort, and enabling engineers to focus on innovation. My role involved close collaboration with cross-functional teams to advance AI-powered automation in electronics development.

Nov 2021 – Oct 2023 · 1 yr 11 mos · Remote

• AI for automating R&D tax processes — explored the use of LLMs to streamline documentation, identify qualifying activities, and improve efficiency in tax credit analysis.

Mar 2021 – Aug 2021 · 5 mos · Remote

• AI for scaling talent delivery — supported GenAI product management to match expert teams with business needs, accelerating implementation through AI-driven talent-as-a-service.

Nov 2020 – Present · 5 yrs 6 mos · Remote

• Delivered multiple end-to-end AI/ML projects across industries, including:
• AI for high-frequency trading — improved returns and reduced volatility by combining LLMs, sentiment analysis, and proprietary signals, with strong compliance and model validation.
• AI for autonomous agents — developed multi-agent LLM systems capable of planning, reasoning, and task execution across tools and APIs, enabling automation of complex workflows.
• AI for personalised recommendations — built a real-time recommendation engine using deep learning and user embeddings, improving engagement and conversion for digital platforms.
• AI for fraud detection — applied graph neural networks and anomaly detection to identify transactional fraud patterns in financial data, increasing detection accuracy while reducing false positives.
• AI for multimodal understanding — engineered a system combining vision and language models (e.g., CLIP, BLIP) to extract structured insights from product images and descriptions, powering smarter search and classification.
• AI for synthetic data generation — designed a generative pipeline to produce domain-specific synthetic datasets using diffusion models and LLMs, reducing data acquisition bottlenecks and improving model robustness.
• AI for customer feedback analysis — implemented NLP pipelines to extract insights from large-scale support tickets and reviews, enabling data-driven product decisions.
• AI for predictive maintenance — applied time-series forecasting and anomaly detection on sensor data to reduce downtime and optimise maintenance schedules for industrial equipment.
• AI for document intelligence — developed LLM-based systems to extract structured insights from contracts, invoices, and reports, accelerating workflows in legal and financial services.

Sep 2019 – Feb 2021 · 1 yr 5 mos · Kazan, Tatarstan, Russia

• I contributed to a project with Innopolis University and Huawei Research Labs, working on machine learning models to improve code analysis and suggestion generation. I helped develop and fine-tune NLP algorithms for programming languages, leveraging techniques like tokenization and semantic analysis. Using deep learning frameworks like TensorFlow and PyTorch, I supported the optimization of models for code quality assessment and auto-completion features.

Nov 2017 – Jan 2018 · 2 mos · Bengaluru, Karnataka, India

• I worked on developing educational content and provided support to students through various channels. I helped improve learning materials and ensured the accuracy of solutions, making them more accessible to students. Through my efforts, I contributed to creating a supportive learning environment.