Top Tech Jobs & Startup Jobs

Build and ship production-ready enterprise software using AI throughout the engineering lifecycle. Develop scalable Jakarta EE applications, APIs, and microservices; integrate AI components; and manage testing, deployment, monitoring, quality, and compliance. Lead AI-native development best practices, make team decisions, mentor engineers, and coordinate cross-functional collaboration. The role requires five or more years of Jakarta EE experience and 15 years of full-time education.

Develop analytical, machine learning, and agentic AI solutions for commerce use cases including pricing, promotions, assortment, personalization, recommendations, and revenue growth. Responsibilities include data discovery, pipeline development, statistical and econometric modeling, optimization, Azure ML deployment, LLM and RAG workflow development, validation, and production integration. The consultant will manage client relationships, communicate data-driven recommendations, and contribute to reusable AI and commerce capabilities.

Develop and enhance custom software applications using Jakarta EE and Java. Design scalable solutions, collaborate with cross-functional teams, analyze requirements, maintain existing systems, troubleshoot performance issues, document code and processes, and support junior team members. The role requires expertise in Jakarta EE, web technologies, SQL, ORM frameworks, object-oriented design, software development lifecycle practices, and agile methodologies.

Owns product vision, roadmap, and backlog while representing customer needs. Uses human-centered design and recurring feedback loops to validate ideas, guide strategic pivots, and improve products. Collaborates with stakeholders and development teams, facilitates alignment, makes team decisions, monitors market trends, and provides cross-team product solutions as a subject matter expert.

Develop and enhance custom Java full-stack software solutions using modern frameworks and agile practices. Design scalable applications, troubleshoot and optimize performance, manage databases, collaborate across teams, make technical decisions, implement software-quality best practices, and mentor junior engineers. The role requires serving as a subject matter expert and contributing to solutions across multiple teams.

Design and implement scalable cloud-native data architectures and batch or streaming pipelines on GCP. Build solutions using BigQuery, Dataflow, Pub/Sub, Apache Beam, Vertex AI Pipelines, and Gemini API integration. Collaborate with architects, data scientists, analysts, and clients to translate requirements into production-ready platforms. Optimize performance, cost, reliability, security, and governance while supporting ML workflows and recommending relevant GCP and AI/ML capabilities.

Develop, code, enhance, troubleshoot, and optimize scalable software applications using Java and Jakarta EE. Collaborate with cross-functional teams in an agile environment, contribute to technical solutions, participate in discussions, and provide guidance to junior team members. Continuously apply modern frameworks, development tools, and software engineering best practices to deliver maintainable solutions aligned with business requirements.

Leads data science, AI/ML, and Agentic AI solutions for commerce clients across pricing, promotions, assortment, personalization, recommendations, and customer activation. Responsibilities include data discovery, statistical and econometric modeling, optimization, ML model development, pipeline management, deployment, and enterprise agent integration. Manages senior client relationships, guides architecture and delivery, mentors teams, and develops reusable AI commerce accelerators and thought leadership.

Build and ship production-ready software using AI throughout the engineering lifecycle. Develop scalable Jakarta EE applications, improve software quality and compliance, and apply cloud-native and microservices practices. Serve as a subject matter expert, lead technical decisions, coordinate across teams, establish AI-native development best practices, and mentor junior engineers. Responsibilities include designing, debugging, testing, performance tuning, and continuously improving enterprise applications and development operations.

Build and ship production-ready enterprise software using AI throughout the engineering lifecycle. Develop scalable, secure, and maintainable applications, integrate AI capabilities, support CI/CD and cloud-native microservices, troubleshoot complex systems, and continuously improve development and operational practices. Collaborate with cross-functional teams, document AI-native methodologies, contribute to solutions, and mentor junior team members.