Software Engineer / Java & Python
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Role details
Tech stack
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Job description
Ability to actively contribute in scrum ceremonies (daily stand-ups, sprint planning, refinements, reviews, retrospectives), ensuring alignment on scope, priorities, dependencies, and delivery outcomes.
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Stakeholder Management & Executive Communication Strong capability to provide clear, concise, and regular updates to stakeholders (business + tech), translating technical progress into business impact, risks, and next steps.
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Release Management & Production Support Hands-on experience managing production releases, supporting deployments, handling incidents, performing root cause analysis (RCA), and coordinating remediation while minimizing downtime and risk.
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Requirements Leadership, Estimation & Sizing Ability to lead requirements gathering discussions with business stakeholders and Product Owners, clarify ambiguity, break down work into deliverable components, and provide sizing/effort estimates.
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Architecture & Distributed Systems Problem Solving Strong practical skills in designing and troubleshooting distributed systems (microservices, event-driven systems), including debugging complex issues across services, queues/streams, and APIs.
Requirements
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8-12+ Years of Professional Software Engineering Experience Demonstrated senior-level experience delivering enterprise-grade applications end-to-end (design, development, testing, deployment, and support) in complex environments.
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Advanced Java Expertise (Java 8+), Spring Boot & Microservices Proven hands-on development in modern Java stacks, including Spring Boot, microservice patterns, dependency injection, resiliency, observability, and scalable service design.
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Hands-on Python for Backend, Automation, and/or AI Tooling Practical Python experience used for building backend services, automation scripts, orchestration, or integrating AI tooling-beyond basic scripting.
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Production Experience with Apache Kafka & Event-Driven Systems Real-world Kafka usage in production environments, including topic design, producer/consumer patterns, error handling, monitoring, and (ideally) exposure to Kafka Streams or Flink.
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AI/ML & LLM Integration Experience (Plus Cloud Exposure/Certs) Experience integrating AI/ML tools, LLM APIs, or ML platforms into applications, and familiarity with the ML lifecycle (data preprocessing, feature engineering, evaluation). Cloud exposure and/or certifications (AWS/Azure/Google Cloud Platform) are a strong plus.
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