NannyML

NannyML is an Open Source Python library to detect silent model failure, estimate the performance of ML model even without ground truth, and robustly identify data drift.

Co-Founder and CEO

Prophecy Labs

Founder

Data Solutions. Elegantly Crafted.

Prophecy Labs helps companies automate complex work, optimize business processes, and predict events that impact your business.

Let our passion, be your edge.

Managing Partner

Essent.be

Developing a data platform in Scala/Spark to deal with large scale data and
deploying large scale machine learning models.

Big Data Engineer

SCK-CEN

This project was to help prepare for long term space travel namely a long term lunar outpost within the following decade. It involved working with gene expression data to help determine the effects of long term isolation and harsh condition on the human immune system. Data from workers of the Concordia platform in antarctica was used. Technical details of the R code can be found at the link below.

Research Bioinformatician 

Building an open source library to estimate the performance of deployed machine learning models in the absence of ground truth.

I love talking about: machine learning, decision making, post deployment data science, performance estimation, and bunch of other stuff. 

currently learning about: causal inference 

Always open to have a chat 🙂

Leave me anonymous feedback -> https://www.admonymous.co/hakim_elakhrass

🥸Hakim Elakhrass

building Soda to monitor the worlds data and decisions

Doctoral Researcher (CS) at University of Helsinki

Last week at the NeurIPS 2025 presenting our paper "Estimating Model Performance Under Covariate Shift Without Labels", which is joint work with Jakub Białek, Wojtek Kuberski, and Nikolaos Perrakis. What a highlight reel of talks, presentations and discussions! For those who can't wait until the proceedings are published, check out the paper in arXiv at https://lnkd.in/dME4FhMT.

ACubetech Solutions Private limited

- Leading the development of large language models (LLMs) tailored specifically for US-based clients by liaising between stakeholders during the whole life cycle, from analysis and clarification of requirements all the way through deployment, and stimulating scalability and optimum performance.

- Evaluating state-of-the-art machine learning and deep learning frameworks for addressing diverse use cases.

- Performing cutting-edge prompt engineering and vector database management for chatbot development using Claude models such as Anthropic, Sonnet, and OpenAI to create contextually aware and responsive conversational agents.

- Creating and improving WordPress-based integrations of AI, including the likes of Meow Apps plugins, for increasing the one-platform scope and capability developed for web-based solutions.

Senior Machine Learning Engineer

Quantiphi

- Architected, developed, and deployed end-to-end ML pipelines for real-time AI applications in mission-critical sectors. Designed models for dynamic pricing, real-time anomaly detection, predictive maintenance, and customer behavior analytics, achieving low-latency performance under stringent SLAs.

- Collaborated with Business Analysts (BAs), Product Owners (POs), and engineering teams to translate complex challenges into scalable AI architectures. Optimized models for sub-millisecond inference, ensuring robustness and resilience.

- Led the development of deep learning frameworks using CNNs, RNNs, and transformer-based models (e.g., BERT, RoBERTa, ELMo) to solve intricate computer vision (multi-object tracking, semantic segmentation), NLP (context-aware embeddings, semantic parsing), and voice analytics tasks.

- Implemented advanced anomaly detection techniques using unsupervised and semi-supervised learning for high-dimensional datasets. Deployed reinforcement learning algorithms to drive autonomous decision-making in non-stationary environments.

- Developed adaptive time series forecasting models using LSTMs, GRUs, and hybrid architectures for high-frequency scenarios like market predictions and supply chain optimization. Integrated continuous retraining to enhance real-time adaptability.

- Built distributed data pipelines with frameworks like Apache Spark and Dask, orchestrating feature engineering, real-time ETL processes, and high-throughput data wrangling for seamless model deployment across diverse ecosystems.

- Established CI/CD pipelines for scalable ML deployments using Docker and Kubernetes, working with DevOps to implement automated model lifecycle management, retraining protocols, and monitoring to counteract model drift.

- Instituted active learning frameworks with dynamic feedback loops, proactive anomaly detection, and adaptive model retraining to maintain relevance and accuracy in evolving environments.

Machine Learning Engineer

Resolve Disputes Online

- Developed an advanced AI-powered chatbot similar to ChatGPT-3 and Google Bard, with a focus on legal mentoring and providing problem-solving suggestions for judicial cases, tailored to a client based in Singapore. The chatbot is designed to understand complex legal terminology, offering users dynamic, context-aware responses.

- Leveraged deep neural networks (DNNs) to architect and optimize the chatbot, focusing on training and refining Natural Language Processing (NLP) models. This involved advanced techniques for text preprocessing, entity recognition, intent detection, and contextual understanding using cutting-edge models and methods.

- Implemented state-of-the-art neural networks such as BERT, Word2Vec, FastText, ELMo, and modern transformer architectures to achieve high accuracy in legal text interpretation and user query handling, integrating models that support semantic search, summarization, and case-based reasoning.

- Integrated the chatbot with existing enterprise systems, using RESTful APIs and ensuring seamless data exchange between various components, enabling real-time legal insights and recommendations to users.

- Responsible for writing, reviewing, and optimizing code across multiple NLP frameworks such as NLTK, spaCy, PyTorch-NLP, Gensim, RoBERTa, AllenNLP, TextBlob, and Pattern. This included implementing and fine-tuning both pre-trained models and custom-built NLP models, ensuring optimal performance for language understanding, legal text processing, and multi-turn dialogue capabilities.

- Continuously improved model accuracy through rigorous hyperparameter tuning, transfer learning, and fine-tuning on domain-specific datasets, ensuring the chatbot meets high standards of relevance and precision in legal advice generation.

Artificial Intelligence Developer

Analance by Ducen

- Designed and implemented end-to-end machine learning pipelines in Python, incorporating advanced analytical techniques such as linear and non-linear regression models, binary and multi-class classification algorithms, unsupervised clustering (K-means, hierarchical clustering), time series analysis (ARIMA, Prophet, LSTM), and association rule mining (Apriori, FP-Growth).

- Actively contributed to Agile Scrum environments, ensuring comprehensive QA reports through continuous integration, and followed test-driven development (TDD) practices with unit testing frameworks like PyTest.

- Collaborated with cross-functional teams including Business Analysts (BAs) and Product Owners (POs) to align machine learning outcomes with defined performance metrics like precision, recall, F1 score, and ROC-AUC.

- Performed detailed data engineering tasks involving complex data wrangling, feature extraction, and ETL processes using tools such as Pandas, NumPy, and other data science platforms. 

- Deployed machine learning models in production environments through CI/CD pipelines, collaborating with DevOps teams. Deployed models as RESTful APIs using Flask or FastAPI, containerized them with Docker, and orchestrated using Kubernetes.

- Developed and maintained automation testing scripts utilizing the Robot Framework, ensuring continuous integration for unit tests, integration tests, and end-to-end testing. Automated workflows using Jenkins and GitLab CI to ensure post-deployment model assessment and functionality validation.

Associate Software Engineer

- Engaged in learning and implementing essential software engineering principles in real-world scenarios, with hands-on exposure to full-stack development frameworks and technologies like Flask, Django, and AngularJS.

- Conducted exploratory data analysis (EDA) on structured datasets, developing basic visualizations and deriving insights to aid in decision-making processes.

- Contributed to the initial phases of machine learning model development by exploring binary and multi-class classification algorithms, focusing on enhancing skills in scikit-learn and TensorFlow.

- Gained proficiency in writing unit tests and maintaining test cases using PyTest and Robot Framework, ensuring early-stage code validation.

Trainee Software Engineer

- Assisted in the development and maintenance of codebases, contributing to small-scale projects focused on foundational coding practices in Python and JavaScript.

- Participated in code reviews, gaining exposure to Agile processes and collaborating with mentors on implementing basic algorithms and data structures.

- Gained exposure to ETL (Extract, Transform, Load) tasks, assisting in the transformation of unstructured data for report generation using SQL and Pandas.

Software Engineering Intern

I'm Sanjay, a seasoned 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭 & 𝐀𝐈/𝐌𝐋 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 with 6+ years of experience crafting resilient machine learning architectures and tackling real-world problems through innovative deep learning applications.

🔍 𝐊𝐞𝐲 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:

𝐓𝐨𝐩 𝐂𝐨𝐝𝐞𝐫: Ranked among the  𝑻𝒐𝒑 𝟱𝟬𝟬 𝒐𝒏 𝑮𝒆𝒆𝒌𝒔𝒇𝒐𝒓𝑮𝒆𝒆𝒌𝒔 𝒂𝒏𝒅 𝑻𝒐𝒑 𝟭𝟬𝟬𝟬 𝒐𝒏 𝑳𝒆𝒆𝒕𝑪𝒐𝒅𝒆, consistently excelling in competitive programming challenges.

𝟭𝐌+ 𝐐𝐮𝐨𝐫𝐚 𝐕𝐢𝐞𝐰𝐬: My content dives into AI trends, in-depth ML algorithms, and advanced Deep Learning engineering. I aim to simplify complex topics for enthusiasts and professionals alike.

𝟭𝟯 𝐈𝐧𝐭𝐞𝐫𝐧𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐇𝐚𝐜𝐤𝐚𝐭𝐡𝐨𝐧 𝐌𝐞𝐝𝐚𝐥𝐬:
- 𝑨𝒅-𝑰𝒏𝒇𝒊𝒏𝒊𝒕𝒖𝒎: 2 Bronze Medals in Mathematical Programming 🥉🥉
- 𝑾𝒆𝒆𝒌 𝒐𝒇 𝑪𝒐𝒅𝒆:  1 Silver and 5 Bronze Medals in Algorithm Contests 🥈🥉🥉🥉🥉🥉
- 𝑾𝒐𝒓𝒍𝒅 𝑪𝒐𝒅𝒆 𝑺𝒑𝒓𝒊𝒏𝒕: 3 Bronze Medals in Algorithm Contests 🥉🥉🥉
- 𝑯𝒐𝒖𝒓-𝑹𝒂𝒏𝒌:: 1 Bronze Medal in Algorithm Contest 🥉 
- Rated in the 𝟵𝟮𝗻𝗱 𝗽𝗲𝗿𝗰𝗲𝗻𝘁𝗶𝗹𝗲 globally on HackerRank, with 100+ challenges solved!

💼 𝐏𝐫𝐨𝐟𝐞𝐬𝐬𝐢𝐨𝐧𝐚𝐥 𝐄𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞:I've been fortunate to gain hands-on experience across diverse environments, from fast-paced startups to established MNCs. I specialize in building end-to-end ML pipelines that address business challenges through predictive modelling, NLP solutions, and computer vision applications. My work focuses on customizing analytics strategies to align with unique business requirements, ensuring impactful and scalable solutions.

✨  𝐎𝐩𝐞𝐧 𝐒𝐨𝐮𝐫𝐜𝐞 & 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡: Passionate about contributing to the community, I've built and shared open-source projects on statistics, ML, and AI, emphasizing innovative architectures and scalable frameworks. 

𝐌𝐞𝐝𝐢𝐮𝐦: https://sanjayjsw05.medium.com/
𝐇𝐚𝐜𝐤𝐞𝐫𝐧𝐨𝐨𝐧:  https://hackernoon.com/u/sanjaykn170396
𝐇𝐚𝐬𝐡𝐧𝐨𝐝𝐞: https://sanjay7907.hashnode.dev/
𝐆𝐞𝐞𝐤𝐬𝐟𝐨𝐫𝐆𝐞𝐞𝐤𝐬: https://www.geeksforgeeks.org/user/sanjaycse_02/
𝐋𝐞𝐞𝐭𝐜𝐨𝐝𝐞: https://leetcode.com/u/sanjayjsw05/
𝐐𝐮𝐨𝐫𝐚: https://www.quora.com/profile/Sanjay-Kumar-563
𝐆𝐢𝐭𝐡𝐮𝐛: https://github.com/Sanjay-Nandakumar

If you're interested in collaboration, exploring ideas, or diving into the dynamic world of AI, ML, and Deep Learning, feel free to drop me a message. Let's connect, innovate, and grow together in this exciting field! 🌱💡🔗

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Sanjay Nandakumar

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Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}🥸Hakim Elakhrass's LinkedIn Performance

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