Machine Learning (ML) Engineer

ML DEVELOPER

Hyperworks Imaging is a cutting-edge technology company based out of Bengaluru, India since 2016. Our team uses the latest advances in deep learning and multi-modal machine learning techniques to solve diverse real world problems. We are rapidly growing, working with multiple companies around the world.

JOB OVERVIEW

We are seeking a talented and results-oriented ML Developer to join our growing team in India. In this role, you will be responsible for developing and implementing new advanced ML algorithms and AI agents for creating AI assistants of the future. 

The ideal candidate will work on a complete ML pipeline starting from extraction, transformation and analysis of data to developing novel ML algorithms. The candidate will implement latest research papers and closely work with various stakeholders to ensure data-driven decisions and integrate the solutions into a robust ML pipeline.

RESPONSIBILITIES:

• Create AI agents using Model Context Protocols (MCPs), Claude Code, DsPy etc.
• Develop custom evals for AI agents.
• Build and maintain ML pipelines
• Optimize and evaluate ML models to ensure accuracy and performance.
• Define system requirements and integrate ML algorithms into cloud based workflows.
• Write clean, well-documented, and maintainable code following best practices

REQUIREMENTS:

• 2-3+ years of experience in data science, machine learning, or a similar role.
• Demonstrated expertise with python, PyTorch, and TensorFlow.
• Graduated/Graduating with B.Tech/M.Tech/PhD degrees in Electrical Engg./Electronics Engg./Computer Science/Maths and Computing/Physics
• Has done coursework in Linear Algebra, Probability, Image Processing, Deep Learning and Machine Learning.
• Has demonstrated experience with Model Context Protocols (MCPs), DSPy, AI Agents, MLOps etc

WHO CAN APPLY:

Only those candidates will be considered who,

• have relevant skills and interests
• can commit full time
• Can show prior work and deployed projects
• can start immediately

Please note that we will reach out to ONLY those applicants who satisfy the criteria listed above.

SALARY DETAILS: Commensurate with experience.

JOINING DATE: Immediate

JOB TYPE: Full-time

Role Summary 

We are seeking an Advanced Machine Learning Engineer to design, build, train, deploy, and operate scalable machine learning systems. This hands-on ML engineering role focuses on implementing models and production systems—not solely conducting data analysis or research. It requires strong Python and deep-learning expertise, experience with frameworks such as PyTorch or TensorFlow, and the ability to own the end-to-end model lifecycle, from behavioral/event data preparation and feature engineering through model development, large-scale training, deployment, inference optimization, and monitoring. The engineer will work with Snowflake, Databricks, Spark, and GPU-based training infrastructure to deliver reliable, efficient, and maintainable ML capabilities. 

Must have - Buulding and operated production ML systems using Python + PyTorch /TensorFlow + Snowflake/Databricks + MLOps. 

Key Responsibilities 

Machine Learning Engineering 

• Implement and optimize machine learning algorithms and models for business use cases, including classification, regression, clustering, and recommendation systems as applicable. 
• Translate model requirements into robust, reusable, and testable Python components. 
• Build model training, validation, scoring, and inference workflows that can be integrated into production applications. 
• Evaluate model performance against agreed technical and business metrics and implement improvements to accuracy, latency, robustness, and resource efficiency. 
• Apply appropriate validation, error analysis, and model testing practices to ensure reliable behavior. 

Train models efficiently on GPU infrastructure using techniques such as mixed-precision training, gradient accumulation, checkpointing, memory optimization, and distributed training strategies where appropriate. 

Use PyTorch, TensorFlow,Pyspark or equivalent frameworks to implement experiments and production-ready training code; apply rigorous evaluation, error analysis, and reproducibility practices. 

Build model-training workflows covering event/token sequence preparation, tokenization, temporal and positional representations, self-supervised pretraining, and downstream fine-tuning when appropriate to the use case. 

Design, implement, train, and optimize deep-learning models; develop custom architectures where required, including transformer- and attention-based models for sequential or behavioral data. 

Deep Learning & Model Architecture 

Data Platforms & ML Pipelines 

• Use Snowflake and SQL to access, query, and prepare enterprise data required by machine learning applications. 
• Use Databricks for scalable data processing, feature preparation, model development, and ML pipeline execution. 
• Develop and maintain reliable pipelines for data preparation, feature generation, training, batch scoring, and inference. 
• Use PySpark and distributed processing techniques where appropriate for large-scale workloads. 
• Work with data engineering teams to integrate upstream data sources and ensure dependable data contracts, quality checks, and pipeline operation. 

Productionization & MLOps 

• Productionize ML solutions and integrate them with services, APIs, data products, or downstream business workflows. 
• Implement model and code versioning, experiment tracking, reproducible builds, automated testing, and release practices. 
• Monitor model outputs and pipeline health; troubleshoot failures, data changes, drift, and performance degradation. 
• Improve operational reliability, scalability, observability, and maintainability of ML systems. 
• Collaborate with platform, cloud, and DevOps teams on deployment automation and production support. 

Build observable production ML systems with appropriate logging, alerting, performance tracking, and operational safeguards. 

Optimize inference latency, throughput, memory use, and serving cost through model architecture choices, batching, and serving-side optimizations. 

Software Engineering & Technical Ownership 

• Develop clean, modular, documented, and maintainable code following engineering standards and design principles. 
• Contribute to technical design, code reviews, testing strategies, and architectural decisions for ML systems. 
• Own assigned ML engineering deliverables from technical design through implementation, release, and ongoing support. 
• Identify performance bottlenecks and improve runtime, resource utilization, and processing efficiency. 
• Collaborate with cross-functional engineering and product teams to deliver dependable ML capabilities. 

Required Skills & Qualifications 

• Strong proficiency in Python and software engineering practices, including object-oriented design, modular development, testing, debugging, and code quality. 
• Strong ML foundation and mathematical understanding 
• Strong understanding of the end-to-end ML lifecycle, including model architecture, training, evaluation, optimization, inference, production integration, and monitoring. 
• Strong SQL skills and experience working with structured data and data pipelines. 
• Hands-on experience with Snowflake for querying and preparing data for ML workflows. 
• Hands-on experience with Databricks for data processing and/or ML pipeline development. 
• Strong hands-on experience with PyTorch, TensorFlow, or a similar deep-learning framework; experience with scikit-learn is also useful where appropriate. 
• Understanding of production ML concepts, including reproducibility, model versioning, deployment, monitoring, and troubleshooting. 
• Ability to design reliable, scalable, and maintainable software components and pipelines. 

Familiarity with GPU training concepts and distributed strategies such as Distributed Data Parallel (DDP), Fully Sharded Data Parallel (FSDP), or equivalents is an advantage for large-model workloads. 

Understanding of model optimization, inference efficiency, and integration with production services or applications. 

Experience developing and deploying ML solutions at scale, including end-to-end model lifecycle ownership. 

Preferred Skills 

• PySpark, Apache Spark, and distributed processing for large-scale behavioral or event data. 
• MLflow or similar tooling for experiment tracking, model registry, reproducible training, and model lifecycle management. 
• Experience integrating Snowflake and Databricks within enterprise ML workflows. 
• Cloud experience with AWS, Microsoft Azure, or Google Cloud. 
• REST APIs, containers, CI/CD, automated testing, logging, and observability. 
• Experience with fraud detection, anomaly detection, behavioral modeling, identity or account-abuse signals, recommendation systems, or other large-scale ML applications. 
• Exposure to Generative AI, LLM integration, embeddings, or RAG as an additional advantage. 

Education & Experience 

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, Computer Engineering, or a related field, or equivalent practical experience. 
• At least 5 years of professional experience building, deploying, and supporting machine learning solutions at scale, or equivalent experience demonstrating the required engineering depth. 
• Demonstrated ability to take ownership of engineering deliverables and work through implementation, deployment, and operational issues. 

ABOUT

The Persona Labs is building a new kind of social platform focused on something most social products do not explicitly optimize for: helping people become real friends.

We want to help people discover interesting people around them, find meaningful common ground, start low-pressure interactions, continue promising conversations, create shared experiences, and ultimately build real-life friendships.

DISCOVER → CURIOSITY → COMPATIBILITY → INTERACTION → UNDERSTAND → IRL EXPERIENCE → FRIENDSHIP

THE AI LAYER - COMPANION INTELLIGENCE

Alongside the platform, we are building a proactive personal AI companion that learns about the user and helps them navigate this journey through personalized recommendations, suggestions, reminders, conversations, and experiences. 

THE OPPORTUNITY

We are looking for a Founding ML Engineer to build the intelligence layer of the platform from the ground up. This is a 0→1 Applied AI / ML role where you will work directly with the founder and Product Engineer to turn ambiguous problems around users, relationships, recommendations and personal intelligence into working systems.

You will be expected to:

Understand the problem → identify the signals → design the intelligence system → prototype → evaluate → deploy → learn → improve.

WHAT YOU WILL BUILD & OWN

USER INTELLIGENCE

User representations, behavioural models, interests, preferences, contextual signals, and evolving understanding of the user. MEMORY Short- and long-term memory, episodic/preference/relationship memory, retrieval, relevance and updating.

RECOMMENDATION & MATCHING

People discovery, compatibility, activity/experience recommendations, and personalized ranking.

INTENT & INTEREST

Infer what the user is trying to do and learn what they care about from behaviour, not only declared interests.

RANKING

Decide what should appear first across potentially thousands of relevant people, activities or experiences.

CONTENT INTELLIGENCE

Classification, toxicity, spam, policy signals, quality, relevance, and semantic understanding.

RELATIONSHIP INTELLIGENCE

Reciprocity, interaction health, shared interests, progression, declining engagement and shared activity.

NEXT-BEST-ACTION

Determine the most useful action now: show a person, suggest a question, recommend an activity, reconnect, or do nothing.

TRUST / SAFETY INTELLIGENCE

Fake-account signals, spam, abuse, behavioural anomalies, risky interactions and moderation assistance.

COMPANION INTELLIGENCE

Use signals and outputs to help the companion decide what to say, suggest, recommend or not do. 

WHAT YOUR DAY-TO-DAY LOOKS LIKE

• Translate ambiguous product problems into ML/AI system designs.

• Build models and intelligence pipelines using behavioural, relational and contextual signals.

• Develop recommendation, matching and personalization systems.

• Design memory and retrieval systems that help the companion understand the user over time.

• Build and evaluate LLM-powered and agentic workflows.

• Decide when to use traditional ML, rules, retrieval, ranking or LLMs.

• Prototype quickly, test assumptions and iterate based on real user behaviour.

• Work closely with the founder and Product Engineer to turn intelligence into product experiences.

• Design APIs and production systems that bring ML/AI capabilities into the application.

• Build evaluation, monitoring and feedback loops so the intelligence improves over time.

WHO SHOULD APPLY

• Experience: 0–4 years’ experience, including exceptional fresh graduates. Strong 1–3 year engineers and experienced 3–4 year product builders are welcome.

• Strong foundations in ML, Python, statistics and software engineering.

• Evidence of Building: Experience with AI/ML projects, recommendation systems, LLM applications or personalization is highly valued.

• Strong evidence of building: Shipped projects, research, hackathons, internships, open source or startup work. 

WHAT WE LOOK FOR

MACHINE LEARNING DEPTH

Can you understand the modelling problem underneath the application?

RECOMMENDATION & PERSONALIZATION

Can you reason about relevance, ranking, cold start and behavioural signals?

AI ENGINEERING

Can you turn LLMs and agents into reliable product capabilities rather than simple API wrappers?

USER INTELLIGENCE

Can you design systems that gradually understand a person from sparse and changing signals?

SYSTEMS THINKING

Can you move from a model to a production system with APIs, data, latency, cost and monitoring?

EVALUATION MINDSET

Can you determine whether the intelligence actually helped the user?

PRODUCT JUDGMENT

Can you decide what the system should do when there is no predefined answer?

SPEED OF EXECUTION

Can you move from idea → prototype → evaluation → production quickly and responsibly?

BUILD WITH US

You will join at a stage where many of the answers do not exist yet. You will not simply implement a model someone else selected; you will help decide how the product learns to understand people.

CAREERS:

Apply with your resume, GitHub, portfolio or shipped work.

https://forms.gle/12YpUSBY2Sqs5xjp8

www.thepersonalabs.com

Kody Technolab Limited is seeking an experienced AI/ML Engineer to design, develop, and deploy cutting-edge Artificial Intelligence and Machine Learning solutions. The ideal candidate will have

strong expertise in Machine Learning, Deep Learning, Generative AI, LLMs, MLOps, and cloud-based AI deployments.

Key Responsibilities

• Design, develop, and deploy Machine Learning and Deep Learning models for classification, regression, recommendation systems, NLP, Computer Vision, and Generative AI applications.

• Build and maintain end-to-end ML pipelines including data preprocessing, feature engineering, model training, validation, evaluation, and deployment.

• Develop AI solutions using PyTorch, TensorFlow, Scikit-learn, Hugging Face, and related frameworks.

• Work with Large Language Models (LLMs) and foundation models such as GPT, BERT, Llama, Claude, and Stable Diffusion.

• Collaborate with product, engineering, and business teams to translate requirements into scalable AI solutions.

• Optimize model performance, scalability, and reliability for production environments.

• Implement MLOps best practices using tools such as MLflow, Docker, Kubernetes, and Kubeflow.

• Stay updated with emerging trends and research in AI, ML, Deep Learning, and Generative AI.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, Mathematics, or a related field.

• 7+ years of hands-on experience in AI/ML product development.

• Strong proficiency in Python and ML frameworks including Scikit-learn, TensorFlow, PyTorch, and Hugging Face.

• Experience with Generative AI, LLMs, GANs, VAEs, diffusion models, and prompt engineering.

• Strong understanding of the ML lifecycle including model training, tuning, deployment, monitoring, and optimization.

• Experience with MLOps tools such as MLflow, Docker, Kubeflow, and CI/CD pipelines.

• Experience with AWS, Azure, or GCP cloud platforms.

• Strong problem-solving and analytical skills.

Preferred Skills

• Fine-tuning and deployment of Large Language Models.

• Experience with RAG (Retrieval Augmented Generation) architectures.

• Contributions to open-source AI projects or research publications.

• Knowledge of model interpretability, data annotation, and feature engineering.

• C++ experience for high-performance AI applications.

Why Join Kody Technolab Limited?

Opportunity to work on innovative AI products, Generative AI solutions, robotics integrations,

and enterprise-scale applications while collaborating with a highly skilled technology team.

Visit the Website to know more about us.

Company Website - Kody Technolab | Deep Tech Company in Robotics & AI Solution

Kody Robots | Robotics Company in India for Autonomous Robots

Job Description:

We are seeking a versatile and highly skilled Lead AI/ML Engineer with deep expertise in Generative AI (GenAI) and Large Language Models (LLMs). This role requires a leader who can take full ownership of the

AI lifecycle—from initial architectural design to final production execution. You will lead the development of scalable AI-powered applications, demonstrating exceptional execution skills and the ability to deliver high-performance results under pressure in demanding production environments.

Machine Learning & LLM Capability:

 End-to-End ML Engineering: Build and manage comprehensive ML pipelines, including data ingestion, preprocessing, training, and evaluation using frameworks like PyTorch, TensorFlow, and Scikit-learn.  Advanced LLM Systems: Design and implement sophisticated LLM-based applications such as autonomous agents, chatbots, and complex automation tools.

 Generative AI Specialization: Architect and optimize Retrieval-Augmented Generation (RAG) pipelines using vector databases like FAISS, Pinecone, or Weaviate.

 Model Optimization: Fine-tune open-source and proprietary models (e.g., LLaMA, GPT) using advanced techniques like LoRA, QLoRA, or instruction tuning.

 Agentic Frameworks: Develop complex agentic workflows utilizing frameworks such as LangChain or LlamaIndex.

 Prompt Engineering: Implement expert-level prompt engineering, tool/function calling, and structured output generation.

 Project Ownership & Execution

 Full Lifecycle Ownership: Take complete accountability for the full ML and GenAI lifecycle, spanning data processing, model development, monitoring, and optimization.

 Architectural Leadership: Drive strategic architectural decisions for AI platforms, ensuring they are modular, scalable, and maintainable.

 Execution Excellence: Write clean, high-performance Python code following strict OOP principles and manage CI/CD pipelines for seamless project execution.

 Leadership & Mentoring: Act as a key technical leader, managing stakeholders and mentoring team members to ensure all project milestones are met with quality.

 System Integrity: Manage model and prompt versioning, experiment tracking, and comprehensive documentation for all pipelines and workflows.

 Performance Under Pressure

 Production Reliability: Ensure all AI systems maintain extreme scalability and performance under heavy production workloads, including both batch and real-time processing.

 High-Pressure Optimization: Rapidly optimize inference latency and system costs for ML and LLM systems to meet urgent business and technical requirements.

 Proactive Problem Solving: Apply strong analytical thinking to address complex challenges such as system drift, hallucinations, and latency in fast-paced environments.

 Robust Guardrails: Implement and manage strict evaluation frameworks and feedback loops to maintain system quality under stress.

Qualifications:

 Bachelor’s or Master’s degree in Computer Science, AI, ML, or a related field.

 Proven expertise in Python, system design, and scalable AI/ML architecture.

 Deep knowledge of NLP, Computer Vision, and Deep Learning models.

 Hands-on experience with Docker, Kubernetes, MLOps, and major cloud platforms (AWS, GCP, or Azure).

About NonStop io Technologies

NonStop io Technologies is a value-driven company with a strong focus on process-oriented software engineering. We specialize in Product Development and have a decade's worth of experience in building web and mobile applications across various domains. NonStop io Technologies follows core principles that guide its operations and believes in staying invested in a product's vision for the long term. We are a small but proud group of individuals who believe in the 'givers gain' philosophy and strive to provide value in order to seek value. We are committed to and specialize in building cutting-edge technology products and serving as trusted technology partners for startups and enterprises. We pride ourselves on fostering innovation, learning, and community engagement. Join us to work on impactful projects in a collaborative and vibrant environment.

Brief Description:

We're seeking an AI/ML Engineer to join our team. As AI/ML Engineer, you will be responsible for designing, developing, and implementing artificial intelligence (AI) and machine learning (ML) solutions to solve real-world business problems. You will work closely with engineering teams, including software engineers, domain experts, and product managers, to deploy and integrate Applied AI/ML solutions into the products that are being built at NonStop io. Your role will involve researching cutting-edge algorithms and data processing techniques, and implementing scalable solutions to drive innovation and improve the overall user experience.

Responsibilities

● Applied AI/ML engineering; Building engineering solutions on top of the AI/ML tooling available in the industry today. Eg: Engineering APIs around OpenAI

● AI/ML Model Development: Design, develop, and implement machine learning models and algorithms that address specific business challenges, such as natural language processing, computer vision, recommendation systems, anomaly detection, etc.

● Data Preprocessing and Feature Engineering: Cleanse, preprocess, and transform raw data into suitable formats for training and testing AI/ML models. Perform feature engineering to extract relevant features from the data

● Model Training and Evaluation: Train and validate AI/ML models using diverse datasets to achieve optimal performance. Employ appropriate evaluation metrics to assess model accuracy, precision, recall, and other relevant metrics

● Data Visualization: Create clear and insightful data visualizations to aid in understanding data patterns, model behaviour, and performance metrics

● Deployment and Integration: Collaborate with software engineers and DevOps teams to deploy AI/ML models into production environments and integrate them into various applications and systems

● Data Security and Privacy: Ensure compliance with data privacy regulations and implement security measures to protect sensitive information used in AI/ML processes

● Continuous Learning: Stay updated with the latest advancements in AI/ML research, tools, and technologies, and apply them to improve existing models and develop novel solutions

● Documentation: Maintain detailed documentation of the AI/ML development process, including code, models, algorithms, and methodologies for easy understanding and future reference.

Qualifications & Skills

● Bachelor's, Master's, or PhD in Computer Science, Data Science, Machine Learning, or a related field. Advanced degrees or certifications in AI/ML are a plus

● Proven experience as an AI/ML Engineer, Data Scientist, or related role, ideally with a strong portfolio of AI/ML projects

● Proficiency in programming languages commonly used for AI/ML. Preferably Python

● Familiarity with popular AI/ML libraries and frameworks, such as TensorFlow, PyTorch, scikit-learn, etc.

● Familiarity with popular AI/ML Models such as GPT3, GPT4, Llama2, BERT etc.

● Strong understanding of machine learning algorithms, statistics, and data structures

● Experience with data preprocessing, data wrangling, and feature engineering

● Knowledge of deep learning architectures, neural networks, and transfer learning

● Familiarity with cloud platforms and services (e.g., AWS, Azure, Google Cloud) for scalable AI/ML deployment

● Solid understanding of software engineering principles and best practices for writing maintainable and scalable code

● Excellent analytical and problem-solving skills, with the ability to think critically and propose innovative solutions

● Effective communication skills to collaborate with cross-functional teams and present complex technical concepts to non-technical stakeholders

Greetings!

Hiring For Large Product Based Company!

Role- Mlops Engineer

Experience- 8-12 years

Location- Pune, Nagpur

JD-

• 8-10 years of experience in DevOps, MLOps, Data Engineering, Software Engineering or Site Reliability Engineering  
• Strong understanding of cloud infrastructure and experience working with at least one major cloud provider, preferably Azure  

Proficiency in at least one objected-oriented programming language, preferably python with hands-on experience in ml frameworks like TensorFlow, PyTorch or Scikit-learn  

Role Overview

We are looking for an AI/ML Engineer with 2 - 6 years of industry experience to develop and deploy machine learning solutions for industrial and smart manufacturing use cases. The role involves working with real-world industrial data, developing predictive and analytical models, and translating business and operational requirements into scalable AI/ML solutions.

The ideal candidate should have strong fundamentals in machine learning, Python, data processing, model development, and deployment, along with an interest in applying AI to manufacturing, industrial automation, and operational optimization.

Key Responsibilities

• Design, develop, train, and evaluate machine learning models for industrial and manufacturing use cases.
• Analyze large and complex datasets to identify patterns, trends, anomalies, and opportunities for optimization.
• Perform data preprocessing, feature engineering, model selection, and performance evaluation.
• Develop solutions for use cases such as predictive maintenance, anomaly detection, quality prediction, process optimization, forecasting, and equipment monitoring.
• Work with structured and time-series data generated from industrial equipment, machines, sensors, and operational systems.
• Develop and optimize ML pipelines for data preparation, model training, validation, and deployment.
• Collaborate with domain experts, data engineers, software engineers, and business stakeholders to understand requirements and translate them into technical solutions.
• Deploy machine learning models into production environments and monitor model performance.
• Troubleshoot model and data-related issues and continuously improve model accuracy, reliability, and scalability.
• Develop reusable code, APIs, and components to integrate ML models with enterprise and industrial applications.
• Document models, methodologies, experiments, results, and technical implementations.
• Stay updated with emerging AI/ML techniques, industrial AI trends, and smart manufacturing technologies.

Required Technical Skills

• Strong programming experience in Python.
• Good understanding of Machine Learning algorithms and concepts.
• Experience with libraries/frameworks such as Scikit-learn, Pandas, NumPy, and preferably TensorFlow or PyTorch.
• Strong understanding of data preprocessing, feature engineering, model training, validation, and evaluation.
• Experience working with time-series data is preferred.
• Good understanding of statistical concepts and data analysis.
• Experience with SQL and relational databases.
• Understanding of model deployment and productionization of ML solutions.
• Familiarity with Git and software development best practices.
• Good problem-solving and analytical skills.

Preferred Skills

• Experience in Industrial AI, Smart Manufacturing, Industry 4.0, or Industrial IoT (IIoT).
• Experience with predictive maintenance, anomaly detection, forecasting, or quality inspection.
• Exposure to sensor data, machine/equipment data, telemetry, or real-time industrial data.
• Knowledge of computer vision for manufacturing or quality inspection use cases.
• Exposure to cloud platforms such as AWS, Azure, or GCP.
• Familiarity with Docker, Kubernetes, or CI/CD for ML deployment.
• Knowledge of MLOps concepts and tools.
• Exposure to Generative AI/LLMs is an added advantage. 

• Senior Machine Learning Engineer – Vertex AI
• Senior Generative AI Engineer – GCP
• Google Cloud AI/ML Engineer
• MLOps Engineer – Vertex AI
• LLM Engineer – RAG, Gemini, and Vertex AI

• Bachelor’s degree in computer science, Data Science, Information Systems, or a related field  
• 8-10 years of experience in DevOps, MLOps, Data Engineering, Software Engineering or Site Reliability Engineering  
• Strong understanding of cloud infrastructure and experience working with at least one major cloud provider, preferably Azure  
• Proficiency in at least one objected-oriented programming language, preferably python with hands-on experience in ml frameworks like TensorFlow, PyTorch or Scikit-learn