Machine Learning Engineer

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

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