Overview
Lead the design, development, deployment, and operationalization of production-grade machine learning and advanced analytics solutions. Guide ML solution architecture, MLOps practices, technical standards, and mentoring across the team.
What you'll do
- Lead machine learning model design, development, evaluation, and deployment.
- Translate business and product requirements into analytical approaches, feature sets, evaluation methods, and deployment plans.
- Build reusable pipelines for data preparation, feature engineering, training, validation, tuning, and model packaging.
- Implement MLOps practices including experiment tracking, versioning, CI/CD, monitoring, drift detection, retraining, and rollback readiness.
- Partner with engineering teams to integrate models into applications, APIs, workflows, and business systems.
- Establish production monitoring for accuracy, drift, latency, stability, explainability, and business outcomes.
- Mentor data scientists and ML engineers on modeling rigor and production readiness.
What you'll need
- 8+ years of experience in data science, machine learning, applied AI, or advanced analytics.
- Hands-on experience delivering production-grade machine learning models.
- Strong expertise in supervised and unsupervised learning, feature engineering, validation, tuning, and performance interpretation.
- Strong Python skills and experience with common ML and data science libraries.
- Experience building end-to-end ML pipelines and applying MLOps practices.
- Experience with SQL, batch pipelines, streaming data, APIs, and application services.
- Ability to guide technical decisions, explain trade-offs, and mentor other data scientists or ML engineers.
Nice to have
- Experience leading complex ML initiatives involving multiple models and cross-functional teams.
- Experience with forecasting, optimization, recommendation systems, anomaly detection, NLP, or applied AI.
- Familiarity with LLM-assisted analytics, embeddings, retrieval-enhanced workflows, or hybrid ML and GenAI solutions.
- Experience with feature stores, explainability frameworks, responsible AI toolkits, and model governance.
- Working knowledge of distributed training, large-scale data processing, and production performance optimization.
Details
- Location: Bangalore, Karnataka, India.
Read the full description and apply on the company’s own careers page.
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