Company Background Our client is an early-stage technology startup focused on helping enterprises operationalize customer identity data. The platform leverages machine learning and advanced indexing to improve data reliability, accessibility, and cost efficiency across digital and physical touchpoints. Project Description The project focuses on building and integrating large-scale web services for enterprise clients. You will work closely with the founding and product teams to define architecture, API design, deployment strategies, and overall technical direction. The position includes hands-on development, system design, customer integration, and participation in shaping the company’s long-term technical roadmap. Technologies - Java (Spring Boot, J2EE) - Python (nice to have) - CI/CD pipelines - Relational & NoSQL databases - Message queues - Cloud platforms (AWS, Azure, or similar) - Docker, Kubernetes - Service-oriented / microservices architecture What You'll Do - Design and build scalable web services and APIs - Integrate enterprise systems and support data ingestion pipelines - Analyze and improve customer system architectures - Own services from development through production deployment - Collaborate with product and engineering teams on roadmap and architecture - Support technology selection and system design decisions - Participate in Agile development and deliver value quickly - Mentor engineers and support team scaling Job Requirements - 4+ years of experience in software engineering - Strong experience with Java and Spring Boot - Experience with CI/CD pipelines and DevOps practices - Experience with relational and NoSQL databases - Experience building scalable distributed systems - Strong understanding of cloud-native architectures - Experience with Docker and/or Kubernetes - Experience designing and integrating enterprise APIs - Strong coding skills and experience with test automation frameworks - Strong communication skills and ability to work with external clients - Ability to work in a fast-paced, startup environment - English level: B2 (Upper-Intermediate) or higher What Do We Offer The global benefits package includes: - Technical and non-technical training for professional and personal growth; - Internal conferences and meetups to learn from industry experts; - Support and mentorship from an experienced employee to help you professional grow and development; - Internal startup incubator; - Health insurance; - English courses; - Sports activities to promote a healthy lifestyle; - Flexible work options, including remote and hybrid opportunities; - Referral program for bringing in new talent; - Work anniversary program and additional vacation days.

• Develop applications to include user interfaces, workflows, and APIs supporting DHS geospatial systems. • Design, build, and maintain applications that integrate with geospatial data platforms, databases, and enterprise systems. • Support development and maintenance of geospatial applications across cloud and on-premise environments. • Develop and maintain scripts and automated workflows to support data processing, validation, and integration. • Support integration of geospatial data services, including REST-based services and APIs. • Collaborate with cross-functional teams to translate mission and business requirements into technical solutions. • Perform testing, debugging, and troubleshooting to ensure application performance, reliability, and scalability. • Participate in system design, development, and implementation activities across the software development lifecycle. • Support deployment, configuration, and maintenance of geospatial applications and tools. • Ensure compliance with DHS standards, security requirements, and system architecture guidelines. • Contribute to documentation including system design, development processes, and application functionality.

We are looking for a Senior AI/GenAI Engineer with strong hands-on experience building and deploying production-grade AI solutions on Azure. This role is responsible for designing end-to-end AI architectures, leading technical decisions, and translating business needs into scalable, reliable, and secure GenAI systems. You will work closely with cross-functional teams to deliver AI-powered solutions (copilots, AI agents, RAG pipelines) that support both internal operations and client-facing services. Responsibilities - Design, build, and deploy production-grade GenAI solutions (AI agents, copilots, RAG pipelines) with a focus on scalability, reliability, and performance - Own end-to-end architecture of AI systems on Azure, including integration with enterprise data platforms and backend services - Lead the development of LLM-powered applications using Azure-native services and modern orchestration frameworks - Implement LLMOps practices, including monitoring, logging, evaluation, prompt/version management, and cost optimization - Translate business requirements into technical AI solutions and define scalable implementation strategies - Ensure security, governance, and compliance across all AI solutions - Optimize AI systems for latency, cost, and accuracy in production environments - Provide technical leadership, mentor engineers, and drive best practices in system design and code quality

We are looking for a Senior AI/GenAI Engineer with strong hands-on experience building and deploying production-grade AI solutions on Azure. This role is responsible for designing end-to-end AI architectures, leading technical decisions, and translating business needs into scalable, reliable, and secure GenAI systems. You will work closely with cross-functional teams to deliver AI-powered solutions (copilots, AI agents, RAG pipelines) that support both internal operations and client-facing services. Responsibilities - Design, build, and deploy production-grade GenAI solutions (AI agents, copilots, RAG pipelines) with a focus on scalability, reliability, and performance - Own end-to-end architecture of AI systems on Azure, including integration with enterprise data platforms and backend services - Lead the development of LLM-powered applications using Azure-native services and modern orchestration frameworks - Implement LLMOps practices, including monitoring, logging, evaluation, prompt/version management, and cost optimization - Translate business requirements into technical AI solutions and define scalable implementation strategies - Ensure security, governance, and compliance across all AI solutions - Optimize AI systems for latency, cost, and accuracy in production environments - Provide technical leadership, mentor engineers, and drive best practices in system design and code quality