Machine Learning Engineer – Personalization, Recommendation Systems & AI Platforms

Skills:
Machine Learning | Recommendation Systems | Personalization | Deep Learning | MLOps | Distributed Systems | Real-time Inference | GenAI

Department: AI / Machine Learning Engineering
Employment Type: Full Time
Work Mode: Onsite / Hybrid / Remote
Experience: 6–12 Years

About the Role

We are looking for a highly skilled Machine Learning Engineer to design and build scalable, production-grade ML systems powering personalization, recommendations, and intelligent user experiences at scale.

This role sits at the intersection of machine learning, backend engineering, and large-scale distributed systems, focusing on delivering real-time, low-latency AI solutions that impact millions of users.

You will work closely with cross-functional teams to translate business objectives into robust ML systems, driving innovation in AI-driven personalization and decisioning platforms.

What You’ll Do Machine Learning & Personalization Systems Design and develop advanced recommendation systems (ranking, retrieval, collaborative filtering, embeddings, deep learning models) Build personalization engines for dynamic user-specific content delivery Develop models for real-time decisioning and prediction systems End-to-End ML Lifecycle Ownership Own the complete ML lifecycle: Problem definition Data exploration Model development Deployment Monitoring and optimization Continuously improve model performance through experimentation and iteration Scalable Systems & Real-Time Inference Build low-latency, high-throughput ML systems Design infrastructure to support real-time personalization at scale Optimize systems for performance, reliability, and scalability MLOps & Production Excellence Implement model versioning, monitoring, and retraining pipelines Build CI/CD pipelines for ML systems Ensure observability, governance, and reliability of ML deployments Data Engineering & Distributed Systems Design and manage large-scale data pipelines using distributed systems (Spark, Hadoop) Process and analyze massive datasets efficiently Optimize data workflows for ML use cases Cross-Functional Collaboration Work with product managers, data scientists, and engineers Translate business requirements into scalable ML solutions Drive innovation through collaboration and experimentation Innovation & Emerging Technologies Stay updated with advancements in: AI/ML and Deep Learning Generative AI and LLMs AI agents and automation systems Bring cutting-edge ideas into real-world production systems Must-Have Qualifications Experience: 6–12 years in Machine Learning Engineering or Backend Engineering Strong expertise in recommendation systems and personalization models Solid understanding of ML algorithms such as collaborative filtering, ranking models, embeddings, deep learning, and reinforcement learning Hands-on experience with TensorFlow, PyTorch, or Scikit-learn Strong programming skills in Python, Java, or Scala Experience with distributed systems such as Apache Spark and Hadoop Strong understanding of MLOps practices including deployment, monitoring, and lifecycle management Strong analytical and problem-solving skills Good-to-Have Experience with real-time inference systems and low-latency pipelines Exposure to Generative AI, LLMs, or AI agents Knowledge of MapReduce paradigms and large-scale data optimization Experience with A/B testing and experimentation platforms What We Look For Strong engineering mindset with ML system design expertise Ability to build production-grade scalable AI solutions Passion for solving high-scale, real-time problems Strong collaboration and communication skills Ability to thrive in fast-paced, innovation-driven environments Why This Role is High Impact Build AI systems that impact millions of users in real time Work on advanced personalization and recommendation engines Solve complex large-scale engineering and ML challenges Contribute to next-generation AI-powered digital experiences #MachineLearning #MLEngineer #AIJobs #RecommendationSystems #Personalization #MLOps #DeepLearning #DataEngineering #GenAI #LLM #HiringNow #TechCareers #AIEngineering #BigData #CloudComputing