Innovexis is looking for a passionate and motivated Machine Learning Intern to join our innovative team in Bengaluru. This remote internship offers a unique opportunity to gain hands-on experience in the exciting field of education management through the application of machine learning technologies. As a Machine Learning Intern, you will assist in the development and implementation of machine learning models that enhance our educational platforms. You will collaborate with experienced professionals to work on real-world projects that aim to improve learning outcomes and optimize educational processes. This internship will provide you with invaluable skills and insights into the intersection of technology and education. We offer a competitive salary range of ₹20,000 - ₹25,000 per month, along with a supportive environment for personal and professional growth in the fast-evolving education sector. Responsibilities - Assist in the design and development of machine learning algorithms - Analyze datasets to extract meaningful insights - Collaborate with the data science team to implement models - Participate in research and development of new machine learning techniques - Test and validate machine learning models for accuracy and performance - Document processes and results for future reference - Stay updated on the latest trends in machine learning and education technology - Contribute to team meetings and brainstorming sessions Requirements - Currently pursuing or recently completed a degree in Computer Science, Data Science, or a related field - Basic understanding of machine learning concepts and algorithms - Familiarity with programming languages such as Python or R - Experience with data analysis and visualization tools - Strong analytical and problem-solving skills - Excellent communication and teamwork abilities - Ability to work independently in a remote environment - Enthusiasm for learning and applying new technologies

mSupply is North America’s leading distributor of OEM repair parts and equipment, serving professionals in the appliance, HVAC, plumbing, commercial kitchen and pool/spa industries. Headquartered in St. Louis, MO, mSupply is a multi-billion dollar enterprise offering an extensive product range, industry expertise and seamless service. With more than 2,000 associates across the U.S. and Canada, mSupply’s family of brands delivers with speed, reliability and precision through its branches, distribution centers and extensive fleet of delivery vehicles. Shipped orders reach 93% of U.S. customers via next-day ground delivery and 100% within two days. For more information, visit mSupply.com. The ML/AI Engineer designs, builds, and operationalizes machine learning models and AI-powered solutions that drive measurable business value across mSupply’s distribution operations. Working within the enterprise data platform (Microsoft Fabric, dbt, Power BI), this role partners with Data Scientists, Data Engineers, and Business Product Owners to turn analytical models into reliable, scalable production systems. You will bridge the gap between data science experimentation and production-grade AI. Your models will directly influence inventory positioning, demand forecasting, pricing strategy, and customer segmentation across a multi-location distribution network. If you want to build ML systems that run at scale in a real operational environment, not just prototypes that never ship, this is the role. Job Duties & Responsibilities - Design, develop, and deploy machine learning models into production, including forecasting, classification, clustering, recommendation, and optimization systems. - Build and maintain MLOps pipelines covering data preparation, feature engineering, model training, evaluation, versioning, deployment, and monitoring. - Design and build applications using LLMs and generative AI, including RAG pipelines, semantic search, document intelligence, and AI-assisted workflows. - Implement prompt engineering, fine-tuning, and evaluation frameworks for LLM-based features, integrating Azure OpenAI Service into platform workflows. - Collaborate with Data Engineers to design feature stores and curated feature pipelines in the Gold layer of the medallion architecture. - Partner with Data Scientists to translate proof-of-concept models into production-ready engineering, and with Business Product Owners to define and prioritize ML use cases. - Implement model monitoring and alerting frameworks to detect drift, degradation, and data quality issues in production. - Package and serve models via REST APIs, batch inference pipelines, or embedded integrations within the Fabric and Azure ecosystem. - Establish MLOps best practices including CI/CD for model pipelines, experiment tracking, model registry management, and reproducible training workflows. Qualifications Required - Bachelor’s degree in Computer Science, Software Engineering, Mathematics, Statistics, Data Science, or a related quantitative field. - 3+ years of experience in ML engineering, MLOps, or applied AI engineering with production deployments. - Strong proficiency in Python and ML libraries such as scikit-learn, XGBoost, LightGBM, PyTorch, or TensorFlow. - Hands-on experience building and maintaining MLOps pipelines, including experiment tracking, model versioning, and CI/CD for ML workloads. - Solid software engineering fundamentals: version control (Git), testing, code reviews, and modular design. - Experience with cloud-based ML platforms (Azure Machine Learning, AWS SageMaker, or equivalent). - Proficiency in SQL and experience working with large-scale structured datasets. Preferred - Experience with Microsoft Fabric, Azure Synapse Analytics, or Azure OpenAI Service. - Hands-on experience with LLMs, RAG architectures, vector databases, and prompt engineering. - Experience with dbt or similar transformation frameworks in a medallion architecture. - Background in wholesale distribution, supply chain, HVAC, plumbing, or related B2B sectors. - Familiarity with forecasting frameworks such as Prophet, NeuralProphet, or Nixtla. - Experience or demonstrated ability using AI-powered tools (e.g., code assistants, LLMs, AI-augmented workflows) to accelerate work and improve outcomes. Physical Demands & Work Environment This position may require over 40 hours per week and includes regular physical activity such as: - Primarily sedentary work, requiring extended periods of sitting while working at a computer - Ability to communicate effectively in person, via phone, and through virtual collaboration tools - Occasional standing, walking, and movement during meetings, site visits, or travel - Ability to lift and carry light materials (up to 15 lbs) occasionally, such as a laptop or presentation materials - Ability to travel periodically for business needs, including visiting distribution centers, suppliers, stores, or industry events - Office environment includes standard business equipment such as computers, phones, and conferencing tools What We Offer: We prioritize your well-being from day one with a comprehensive benefits package that includes: - Medical, dental, vision, and prescription coverage effective immediately - 401(k) plan with company contributions - Life insurance and short-term disability coverage - HSA/FSA options and an Employee Assistance Program (EAP) - Paid time off, including vacation, holidays, and personal days - Weekly pay, employee discounts, and more Equal Employment Opportunity & Pre-Employment Requirements mSupply is an Equal Opportunity Employer. We make employment decisions without regard to sex, age, race, color, creed, religion, national origin, citizenship or immigration status, sexual orientation, gender identity or expression, disability, genetic information, marital status, veteran or military status, or any other status protected by applicable federal, state, or local law. We are committed to providing reasonable accommodations for qualified individuals with disabilities and to applicants with sincerely held religious beliefs, in accordance with applicable law. To request a reasonable accommodation, please contact careers@msupply.com. Final offers of employment may be contingent upon completion of job-related pre-employment checks and screenings permitted by law for the position. For roles that require operation of a company vehicle, a Motor Vehicle Record (MVR) check may also be conducted to determine insurability. This employer participates in E-Verify to confirm employment eligibility in the United States.

• Take ownership of the design and implementation of modern AI stack components, including data ingestion for AI/ML workloads and end-to-end model training and serving pipelines. • Build and manage fault-tolerant AI platforms that scale economically. You will balance the maintenance of legacy models with the rapid development of advanced, scalable solutions. • Provide technical mentorship to junior engineers and foster a collaborative environment. You will act as a bridge between data science and production engineering. • Promote best practices in coding, testing, and MLOps. You thrive in ambiguous conditions by independently identifying opportunities to optimize model pipelines and improve AI workflows. • Partner with data scientists, product managers, and software engineers to translate business needs into technical requirements and integrate AI solutions into production applications. • Enforce model quality standards, integrity, and reliability. You will be responsible for implementing model lineage, fairness, and privacy controls within the automated pipelines. • Build monitoring frameworks to track model performance and system KPIs, ensuring our AI initiatives drive measurable business outcomes.

• You will lead development of new fraud prediction models using a mix of approaches for tabular, graph, and behavioral data • You will build and scale feature pipelines and training datasets from proprietary and third-party signals, partnering with data and platform teams when needed. • You will prototype new modeling ideas and features, run offline experiments, and drive the best-performing approaches into production with appropriate risk controls. • You productionize models: integrate into batch and/or real-time decision systems, and improve reliability, latency, and operational robustness. • You will instrument and monitor model and data health, and help define retraining/backtesting workflows as fraud patterns evolve. • Identify and implement foundational improvements to how the team builds models. • You will collaborate across Engineering, Fraud Analytics, Product, and ML Platform to define requirements, evaluate tradeoffs, and communicate results clearly to both technical and non-technical audiences.