Hrishikesh Jadhav
AI Engineer, Germany
Open to AI engineering roles in Germany from October 2026. EU Blue Card holder, authorised to work in Germany.
Experience
- 10/2025–09/2026
AI Engineer, Toku
Global employer-of-record and payroll infrastructure platform operating across 46 countries.
- Shipped AI tooling into daily production across product, engineering and sales teams: an MCP server exposing internal systems to LLM clients, a RAG assistant (FastAPI, LangChain, pgvector), and n8n automations for onboarding, payslip processing and support triage. Owned deployment, documentation, onboarding and hands-on user support.
- Cut payroll data intake from hours of manual work per cycle to minutes across production records by building a self-hosted LLM extraction pipeline (Python, FastAPI, PostgreSQL, DigitalOcean) that masks personal data before inference.Self-hosted so that no employee data leaves controlled infrastructure.
- Kept field-level extraction accuracy above the agreed threshold across every extracted field, checked against a large golden evaluation set, by building the evaluation harness and a CI regression gate that blocks any deploy below it.So a change that lowers extraction accuracy cannot reach payroll data.
- Moved security review to pull-request time across thousands of pull requests by integrating automated LLM-assisted security review into GitHub CI for Toku's financial platform.
- Built status.toku.com, Toku's public status platform: production components probed from the Cloudflare edge on a short fixed interval, automated incident detection and resolution, dependency rollups, edge-vs-origin failure isolation, and a JSON API and RSS feed. New incidents notify the team on Slack, and failures that need a code fix are picked up by a coding agent.Probing the edge and the origin separately localises a failure to the CDN or the application before anyone looks at it.
- Built Toku's customer-facing help centre and AI assistant (toku.com/help, Next.js) on Toku's Notion knowledge base, covering payroll, benefits, policy and country-guide articles across dozens of countries, with general-help and visa-support content in separate retrieval corpora and multi-model fallback across providers.Separate corpora so immigration and work-authorisation questions never resolve against general payroll content.
- Brought LLM inference costs down to a fraction of the estimated cost of equivalent third-party API usage by deploying open-weight models on self-hosted GPU infrastructure.
Personal data is masked before inference, and a golden-set gate decides what deploys. - 08/2022–09/2025
Data Scientist, GfK - An NIQ Company
Working Student, Data Science, 08/2022–07/2024
- Reduced manual effort in product-taxonomy classification by around 30% by building an LLM classification service on Amazon Bedrock using prompt engineering and RegEx-based post-processing.
- Reduced catalogue lookup time by around 40% versus keyword search by building vector-embedding semantic retrieval over internal product catalogues.
- Improved simultaneous-viewer prediction accuracy by around 20% over the incumbent model by training and deploying a CatBoost model using 30+ engineered features from sociodemographics, temporal patterns and programme metadata.
- Kept production scoring pipelines running 24/7 without manual intervention by orchestrating S3 ingestion, feature generation, model scoring and automated integration tests in GitLab CI/CD, containerised with Docker on Linux.
- Built a TV-audience data-fusion pipeline using K-Nearest Neighbors and a genetic algorithm to match and clone panel households against large-scale return-path data.
- 04/2020–08/2020
Software Developer (Internship), Sapio Analytics Pvt. Ltd.
- Built COVID-19 decision-support models in Python (SEIRD, scikit-learn, SciPy) and forecasting dashboards (Plotly, AWS) used to inform Government of India lockdown and testing-strategy decisions, with a 4.8% RMSE reduction against the prior baseline.
Projects
Internal RAG learning assistant
NIQ/GfK HACKFEST 2024, Top 3. Built and demoed a retrieval-augmented learning assistant over internal knowledge within 24 hours, with document retrieval and audio ingestion via Whisper (LangChain, vector database, GPT, FastAPI).
Education
- 2021–2024
M.Sc. Artificial Intelligence Engineering (coursework and thesis)
- 04/2022–10/2022
Honours Degree in Entrepreneurship
- 2018–2021
B.E. Computer Science
Research
- 2025
- 2021
Awards
- 2024
NIQ/GfK HACKFEST · Top 3, with a RAG learning assistant built and demoed in 24 hours
- 2024
BMW Innovation Challenge · Selected participant, 24-hour challenge at BMW iFactory Dingolfing (DocCheck use case)
- 2020
IEEE Machine Learning Hackathon · 1st place
- 2020
HackCovid-19 · Winner among 130 teams
- 2017
Smart India Hackathon · Winner (Ministry of Defence)
Skills
- AI and LLM
- RAG, MCP, LLM evaluation and regression testing, prompt engineering, vector search, guardrails, LangChain, LangGraph, Claude SDK, OpenAI SDK, Amazon Bedrock, Hugging Face, OpenRouter, CrewAI, n8n
- Backend and data
- Python, TypeScript, Node.js, FastAPI, Flask, Django, Next.js, React, REST APIs, PostgreSQL, Prisma, pgvector, Qdrant, FAISS, Pinecone
- Cloud and DevOps
- GitLab CI/CD, GitHub Actions, Docker, Linux, AWS (Bedrock, S3), GCP, DigitalOcean, Cloudflare, Git
- ML and data science
- PyTorch, scikit-learn, CatBoost, pandas, NumPy, feature engineering, model evaluation, embeddings
- Languages
- English (C1), German (B1), Hindi (native), Marathi (native)
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