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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