Jobs at Shuru

Designs and operates scalable multi-cloud data platforms, real-time and batch pipelines, lakehouse architectures, and distributed processing systems. The role focuses on Kafka, Spark, BigQuery, Airflow, Terraform, Kubernetes, data reliability, governance, security, and warehouse performance. It also leads technical architecture reviews, mentors engineers, and drives engineering best practices.

Own and improve a production Voice AI and LLM application for a regulated insurance organization. Build agentic workflows using prompts, tool calling, structured outputs, and RAG; work with Amazon Connect, Lex V2, and Bedrock; create evaluation suites and quality gates; improve latency, reliability, security, governance, and cost; and implement PII/PHI safeguards for production releases.

Support an ERP migration from Quantum Control to VistaSuite by reviewing and optimizing Oracle SQL/PLSQL extraction scripts, validating CSV and ERP outputs, identifying mapping and completeness issues, investigating source-to-target discrepancies, and reconciling loaded data across purchasing, inventory, production, logistics, repair, and disassembly.

Lead and mentor engineering teams while owning engineering strategy, architecture, scalability, reliability, security, and delivery. Partner with Product, Business, Data, and Technology stakeholders on priorities and trade-offs. Support cloud infrastructure, platform performance, cost optimization, AI adoption, and continuous improvement. The role requires strong system design knowledge, executive communication, and experience in fast-paced product-led or regulated environments.

Lead backend development using Python, Django, and FastAPI to build scalable applications and high-performance REST APIs. Responsibilities include system architecture, clean code development, code reviews, technical discussions, cross-functional collaboration, and mentoring junior developers. The role requires database experience with PostgreSQL, MySQL, MongoDB, and Redis, along with expertise in scalable architecture and backend engineering best practices.

Translates business needs into functional and technical requirements for software delivery. Responsibilities include gathering requirements, creating user stories and process flows, defining system behavior and integrations, analyzing process gaps, supporting QA and user acceptance testing, managing requirement changes, assessing impacts, and maintaining traceability. Collaborates with clients, product teams, engineers, QA teams, and stakeholders throughout Agile software development projects.

Build and maintain Java backend services and APIs for a card issuance platform. Responsibilities include card lifecycle, transactions, settlement, reconciliation, processor and partner integrations, event-driven systems, testing, auditability, operational visibility, code reviews, releases, troubleshooting, and collaboration with product and engineering teams.

Design and maintain API, web, or mobile automation frameworks using Java, Rest Assured, Playwright, and TypeScript. Develop functional, integration, regression, and end-to-end tests; conduct performance testing with K6; validate APIs, databases, and distributed workflows; integrate tests into CI/CD pipelines; manage test execution and defects; debug failures; and collaborate with engineering and product teams to improve quality throughout development and production.

Own the roadmap and development of fraud prevention, detection, and Trust & Safety products. Define requirements, fraud signals, risk scores, alerts, decisioning, and success metrics for real-time transaction monitoring. Partner with Engineering, Data Science, Risk, Compliance, Operations, and Analytics teams to address account takeover, SIM swap, identity, and payment fraud. Analyze fraud trends, false positives, customer friction, and performance metrics to continuously improve controls and balance security with customer experience.

Build and deploy production-grade AI agents, intelligent workflows, automations, and integrations. Develop memory, context, retrieval, prompt, tool, guardrail, evaluation, testing, and observability frameworks. Create secure Python and FastAPI APIs and integrate AI systems with event streams, vector databases, and enterprise platforms. Collaborate with product, engineering, and business stakeholders to turn ambiguous problems into reliable AI products.

Build and ship production-grade AI agents, LLM workflows, and automation systems. Develop Python and FastAPI backend services, implement prompting, RAG, structured outputs, tool calling, and agent workflows, and create evaluation systems to measure accuracy and prevent regressions. Own features from conception through production, debug AI systems using real-world data, and collaborate within small engineering squads using AI-assisted development tools.

Designs, builds, and maintains scalable data pipelines and cloud-based data solutions using Python, SQL, Snowflake, PySpark, and AWS. Develops data ingestion, transformation, analytics, and integration frameworks for large datasets while improving performance, reliability, and data quality. Collaborates with product, business, and QA teams and applies AI-native tools and workflows for coding, testing, reviews, documentation, and productivity.

Build and deploy AI agents, LLM-powered applications, intelligent workflows, automations, backend services, APIs, and integrations. Translate ambiguous business problems into production-ready solutions involving retrieval, memory, tool use, structured outputs, monitoring, evaluation, and error handling. Collaborate with product, engineering, and business stakeholders while experimenting, debugging, and continuously improving AI systems in a fast-moving startup environment.

Develops interactive Power BI dashboards, reports, data models, KPIs, and DAX calculations. Integrates data from Dynamics 365, SharePoint, SQL, Excel, and APIs; transforms data with Power Query; manages Power BI Service, refresh schedules, gateways, and row-level security. Collaborates with stakeholders, optimizes reporting performance, and troubleshoots data issues. The role is remote but requires recurring travel to Dubai based on project needs.

Develop and deploy production-grade AI and machine learning solutions, including LLMs, RAG, transformers, deep learning, recommendation systems, regression, classification, and clustering. Conduct rigorous statistical analyses, apply FATE principles, translate insights into business strategies, and establish ML production best practices. Collaborate with engineering, product, and analytics teams using Python, SQL, Azure, Snowflake, and modern ML frameworks.

Own the engineering, data, platform, and technology strategy for a fast-growing digital business. Lead engineering and data teams, establish technology roadmaps, oversee cloud infrastructure, architecture, scalability, reliability, security, compliance, and technology costs. Partner with executive stakeholders to align priorities, manage investments and vendors, strengthen analytics, and identify AI and automation opportunities.

Design, build, and scale cloud-native applications with AWS and AI/ML capabilities. Develop AI agents and orchestration frameworks, integrate AWS AI services, automate deployments, monitoring, and infrastructure management, and contribute to full-stack development. Apply AI-assisted development tools, implement secure and performant cloud architectures, troubleshoot distributed systems, collaborate cross-functionally, and mentor junior engineers.

Design, build, and maintain scalable Snowflake data pipelines and cloud data solutions. Develop ETL/ELT workflows, SQL queries, stored procedures, and data models; integrate APIs, databases, SaaS platforms, and cloud storage. Optimize Snowflake performance, implement data quality monitoring, support reporting and analytics, and translate stakeholder requirements into reliable data solutions.

Own the roadmap and end-to-end delivery of internal products and growth tools. Responsibilities include identifying business problems, defining requirements and success metrics, prioritizing features, collaborating with engineering and design, conducting user research, tracking adoption and outcomes, managing launches, and experimenting with AI and automation to improve internal processes.

Sources, screens, and selects candidates for technical and leadership roles. Coordinates interviews, assessments, and hiring feedback; maintains candidate pipelines and ATS records; advises hiring managers on requirements, market trends, and candidate profiles; and supports employer branding through outreach and events. This is a full-time remote position requiring experience recruiting for engineering, data, and product roles.