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Protocol Intelligence
Data-driven signals on your job's competitivenessTier-1 brand, Bangalore metro, and visible backend role raise competition, despite senior ML/platform specialization.
Strong bias toward personalization, ML-serving, and distributed systems experience, limiting cross-industry transferability.
Requires 8+ years backend experience and specific distributed systems, ML-serving, and cloud platform expertise.
Job Description
Structured overview of role & requirementsAbout This Role
Lead architecture, design, and development of large-scale backend personalization systems serving hundreds of millions of users with real-time recommendation engines, feature stores, and ML model serving.
Set technical strategy, define best practices, and ensure production excellence including observability, reliability, and adherence to SLAs for personalization platform.
Collaborate cross-functionally with Data Science, ML Engineering, Product, and SRE teams to productionize ML models and drive measurable business outcomes; mentor and elevate engineers.
Minimum Requirements
8+ years of backend engineering experience with expertise in Java, Kotlin, Python, or Go and experience building large-scale distributed production systems.
Bachelor's degree in Computer Science or related field with minimum 4 years software engineering experience, or 6 years software engineering experience without degree.
Strong skills in distributed systems, microservices architecture, Kubernetes, Kafka, cloud platforms (GCP/Azure), and both SQL and NoSQL databases.
Experience in personalization or ML platform technologies including ML model serving (e.g., TensorFlow Serving), real-time feature engineering, and experimentation platforms.
Ideal Candidate Profile
Experienced technical leader able to influence architecture and strategy across multiple teams and disciplines at scale without direct authority.
Strong cross-disciplinary operator bridging backend systems and ML engineering to productionize models and optimize real-time serving pipelines.
Prior exposure to global, large-scale personalization or recommendation systems powering customer experiences in distributed environments.