Global software consulting firm scaling AI and cloud modernization at enterprise
Exadel is a 2,000+ person software consulting firm headquartered in California with delivery centers across 17 countries. The tech stack—Node.js, React, Python, Java, AWS, Databricks, and heavy adoption of generative AI tools (Bedrock, OpenAI, LangChain, Hugging Face)—reflects a shift toward AI-native delivery. Active pain points (legacy monolith decomposition, Synapse-to-Databricks migration, SOC 2 compliance, AWS security hardening) and projects (modernizing SaaS platforms, rebuilding backends, automating Databricks provisioning) signal an engineering org focused on cloud infrastructure consolidation and compliance-heavy engagements, particularly in regulated verticals like oil and gas.
Notable leadership hires: Business Development Director
Exadel delivers end-to-end software consulting, design, engineering, and managed services to enterprise clients seeking technology-driven business transformation. The firm has completed over 3,500 software projects with a 9/10 client satisfaction score. Operating since 1998, Exadel maintains 30+ delivery centers across 17 countries with 2,000+ engineers, architects, and domain specialists. The hiring mix skews heavily engineering (196 of 322 open roles), with growing data (42) and security (8) functions, reflecting expansion in AI, analytics, and compliance-driven work. Specialties include strategy consulting, generative AI applications, digital transformation, and managed services for regulated industries.
Exadel's primary stack includes Node.js, React, Python, Java, AWS, MongoDB, DynamoDB, Docker, and Databricks. The firm is actively adopting OAuth 2.0, JWT, Oracle Cloud Fusion, Databricks, and GitLab CI/CD, while migrating away from Synapse, ASP.NET, and Teradata.
Active projects include modernizing legacy SaaS platforms, rebuilding backends and frontends from scratch, implementing ACL systems, integrating dispatch systems, automating Databricks provisioning, building CI/CD for data engineering, and constructing scalable ML pipelines. Key pain points are legacy monolith decomposition and cloud/compliance optimization.
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