Rhutwij Tulankar

About

Hands-on engineering manager at a marketing company leading a highly senior, distributed team (10 direct reports) while personally coding ~60–70% and owning end-to-end architecture across three interconnected products. Built agentic CRM automation and a reinforcement-learning-driven distribution layer for channel spend/bidding, with a strong focus on scalable design and observability (Prometheus/APM/logging) enabling frequent releases and few production incidents.

Experience

Education

Key Strengths

• Hands-on engineering manager (60–70% coding) with end-to-end architecture ownership across multiple products
• Designs scalable, resilient systems with strong emphasis on observability (Prometheus/APM/logging) to minimize production incidents
• Applied ML/RL to marketing distribution/bidding logic to improve ROI outcomes
• Player-coach leadership for a distributed team (10 direct reports; Eastern Europe + North America) with lightweight oversight for senior engineers
• Translates ambiguous stakeholder requirements into shippable definitions (e.g., defining “successful apply” for attribution with AI-agent retries and ATS timing)
• Improves delivery velocity via architecture decomposition plus CI/CD and frequent release cadence (weekly minor, biweekly major)
• Led 20–25 engineer team building cross-platform AI products/frameworks
• Owned roadmap, delivery, and end-to-end architecture for internal OpenCore framework at Samsung
• Strong cross-org consensus building and stakeholder alignment across multiple Samsung organizations
• Designed language-bridging SDK to reuse legacy Java/C libraries from Python (tradeoff-driven architecture)
• Built robust CI/CD and scenario-based test strategy for stability and backward/forward compatibility across OS and runtime versions
• Improved developer onboarding and DX via API gateway-driven configuration and documentation/tooling
• Resolved performance disagreement for on-device inference by introducing a dedicated TensorFlow.js/JavaScript module

Reference Highlights

• strong technical depth
• meaningful impact relative to level
• drives sound architectural decisions
• elevates overall engineering quality
• created cohesive architecture for fragmented AI workloads
• improved reliability and scalability
• reduced duplication through standardization
• efficient, business-oriented code
• effective player-coach (hands-on + leadership)
• mentors engineers and raises technical bar
• handles ambiguity and drives end-to-end execution
• creates structure that translates research into production-ready assets
• collaborates well with researchers (including more senior peers)
• provides feedback effectively across seniority levels
• strong delivery speed and execution
• dependable during incidents; timely responsiveness
• positive with partners/stakeholders when given the opportunity
• Architected the data engineering platform/framework
• Deep knowledge of big data technologies
• Strong end-to-end ownership through production
• Good technical judgment in choosing tools
• Keen to learn and find the right solution
• Provided good guidance to teammates (juniors and seniors)
• Influenced key architectural decisions
• Drove ambiguous work to shipped outcomes (pipeline packaging/shipping, release approach)
• Strong debugging; found bugs and proposed solutions
• Effective cross-functional coordination across geographies
• Delivered goals; stakeholders were happy