Machine Learning Engineer

Role OverviewWe are looking for a mid-to-senior level ML Engineer who prioritizes /"core/" machine learning and statistical problem-solving. While Generative AI is part of our future, this role is 70% Classical ML and 30% GenAI. You will be responsible for building, deploying, and monitoring models that drive internal sales forecasting and customer-facing intelligence.The ideal candidate has not /"lost touch/" with the fundamentals of statistics and bagging/boosting algorithms while staying current with LLM orchestration.Key ResponsibilitiesModel Development: Design and implement high-performance models using classical ML (70%) and Generative AI (30%).Forecasting & Optimization: Solve business-critical problems related to sales forecasting, recommendation engines, and classification.End-to-End MLOps: Take ownership of the full lifecycle—from data cleaning and feature engineering to deployment, hyperparameter tuning, and model monitoring.Productionalization: Build and maintain ML pipelines and infrastructure on cloud platforms (AWS/Azure/GCP).Collaboration: Work closely with the CTO and engineering teams to integrate AI agents and predictive models into a production environment.Technical RequirementsCore ML Expertise: Deep proficiency in algorithms such as XGBoost, Logistic Regression, Decision Trees, and various Bagging/Boosting techniques.Advanced Python: Strong coding skills with a focus on production-grade ML libraries (Scikit-learn, Pandas, NumPy).MLOps & Cloud: Hands-on experience with AWS SageMaker (or equivalent in Azure/GCP) and model monitoring tools.Generative AI: Experience with LLMs, Prompt Engineering, and frameworks like LangChain or similar orchestration tools.Statistical Foundation: Strong ability to solve complex business problems using first-principles statistics.Education: Background in Computer Science, Statistics, or a related field with a focus on large-scale systems.Interview ProcessTechnical Round 1: Deep dive into ML fundamentals and project architecture.Technical Round 2: Evaluation of ML theory (30%), Live Coding (30%), Project Experience (30%), and Cultural Fit (10%).Final Round: Project discussion and vision alignment with the CTO.Why Join Us?High Impact: Build models that directly influence business outcomes and revenue forecasting.Modern Stack: Work at the intersection of proven classical ML and cutting-edge GenAI.Ownership: Drive your own work independently in a fast-paced, high-growth environment.Competitive Rewards: High-percentile market salary and equity opportunities based on interview performance.

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