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Rhutwij Tulankar
Engineering Manager and ML/Data Architect specializing in scalable data platforms and personalization
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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.
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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
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