YMinds.AI

AI/ML Engineer (2-5 Years Experience | Immediate Joiner | Remote) (Mumbai)

YMinds.AI

Mumbai•Hybrid•2-5 Yrs•Jun 26

Job description

MachineLearningAppliedMLDataSciencePythonDeveloperRecommendationSystemsArtificialIntelligenceForecastingMLEngineerHiringNowTechJobsYMindsAI

Overview

Our client is seeking an AI/ML Engineer to build intelligent decision systems that help businesses make better data-driven decisions. The ideal candidate will have hands-on experience building production ML systems and the ability to translate ambiguous business problems into practical machine learning solutions that create measurable business impact. This role focuses on real-world problem-solving, model evaluation, recommendation systems, ranking systems, retrieval, forecasting, and decision-support workflows rather than simply training models.

Responsibilities

Design, develop, and deploy production-grade machine learning systems
Build recommendation systems, ranking models, and retrieval-based solutions
Develop prediction, forecasting, clustering, anomaly detection, and optimization models
Work with embeddings, similarity search, and representation learning techniques
Build data processing pipelines and production-ready ML workflows
Perform feature engineering, exploratory data analysis (EDA), and behavioral modeling
Define evaluation frameworks and measure both model performance and business impact
Translate business challenges into machine learning problems and identify meaningful signals in complex datasets
Analyze model failures, optimize performance, and improve reliability
Collaborate with product and engineering teams to deliver scalable, data-driven solutions
Balance model quality, operational constraints, and business objectives when designing solutions

Qualifications

2-5 years of hands-on experience building production ML systems
Experience shipping at least one machine learning-powered feature or workflow into production
Solid understanding of supervised learning, feature engineering, and model evaluation
Experience with recommendation systems, ranking systems, embeddings, and representation learning
Knowledge of experimentation methodologies, model debugging, and performance analysis
Strong understanding of offline and online model evaluation techniques
Solid grasp of bias-variance tradeoffs, precision-recall tradeoffs, calibration, overfitting, and distribution shift concepts
Strong programming skills in Python, Pandas, NumPy, and SQL
Experience building data processing pipelines and working with production codebases
Experience with APIs and data integrations
Ability to work with real-world datasets and measure business impact
Strong analytical thinking, problem-solving, and communication skills

Nice to have

Experience with cloud platforms, distributed systems, orchestration frameworks, or MLOps tools
Experience building recommendation, ranking, search, or retrieval systems
Knowledge of forecasting, optimization, and experimentation frameworks
Experience deploying ML systems into production environments
Background in commerce, marketplace, SaaS, or consumer product domains
Experience building data products with measurable business outcomes
Familiarity with modern AI systems and hybrid ML/AI architectures

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