Job Title

Role Overview We 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 is70% 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 Responsibilities Model 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 Requirements Core ML Expertise:Deep proficiency in algorithms such asXGBoost, 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 withAWS SageMaker(or equivalent in Azure/GCP) and model monitoring tools. Generative AI:Experience with LLMs, Prompt Engineering, and frameworks likeLangChainor 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 Process Technical 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.