A/B Testing, Artificial Intelligence (AI), Click Through Rate (CTR), Continuous Deployment/Delivery, Continuous Integration, Customer Experience, Customer/Client Research, Data Quality, Database Design, Deep Learning, Large-Scale Systems, Mentoring, Metrics, Neo4j, Normalized Discounted Cumulative Gain (NDCG), Performance Modeling, Process Improvement, Python Programming/Scripting Language, RDF (Resource Description Framework), Scalable System Development, Software Engineering, Systems Reliability, Team Player
Mandatory Skills: Hands-on experience in building and maintaining Knowledge graphs and Recommendation systems knowledge
Job Title:
Senior AI/ML Engineer - Personalization & Recommendation Systems Role Overview:
Lead the design and deployment of scalable AI/ML solutions focused on real-time personalization, recommendation systems, and customer knowledge graphs, driving measurable improvements in engagement and conversion.
Key Responsibilities
- Design and build collaborative, content-based, and hybrid recommendation systems
- Develop real-time personalization pipelines and ranking models
- Architect end-to-end ML systems (batch + streaming) with low-latency inference
- Build customer knowledge graphs (Neo4j/Neptune) modeling users, products, and interactions
- Enable Customer 360 insights and context-aware recommendations
- Develop scalable pipelines using Python, Spark, Kafka Implement feature engineering, model training, and deployment workflows
- Drive experimentation (A/B testing) and optimize for CTR, engagement, and conversion Ensure data quality, model performance, and system reliability
- Apply MLOps practices (CI/CD, monitoring, model lifecycle management)
- Mentor team members and collaborate with product/business stakeholders
Required Skills Strong
- Python and experience with Pandas, PySpark Expertise in recommender systems (matrix factorization, deep learning, ranking models)
- Experience in entity resolution / record linkage
- Hands-on with graph modeling & graph databases (Neo4j, RDF, graph embeddings)
- Strong understanding of ML lifecycle, experimentation, and evaluation metrics (NDCG, MAP, Precision/Recall)
Nice to Have
- Experience with real-time ML systems and large-scale data (TB/PB)
- Impact Drive personalized customer experiences, improve engagement & conversion, and enable data-driven decision-making at scale.