Top Tech Jobs & Startup Jobs

Lead the architecture and delivery of enterprise Generative AI solutions using LLMs, RAG, AI agents, cloud-native platforms, and enterprise integrations. Define AI strategies, reference architectures, governance, security, observability, and implementation roadmaps. Guide architecture reviews, mentor engineering teams, evaluate emerging technologies, and drive production delivery across regulated and enterprise environments.

Build and maintain data models, warehouses, and ETL pipelines using SQL, relational databases, big data technologies, cloud data services, and programming languages such as Python, Java, or Scala. Apply .NET development, DevOps, data security, and privacy practices while collaborating on innovative digital solutions. The role may involve national and international travel, professional development, conferences, and potential relocation or international assignments.

Design and lead enterprise-scale Adobe Experience Manager solutions, including architecture, integrations, deployment strategies, CI/CD, Dispatcher, CDN, caching, governance, security, and performance optimization. Lead and mentor development teams, make architectural decisions, and collaborate with stakeholders to deliver scalable digital experiences. Required expertise includes AEM, Java, OSGi, Sling, JCR, REST APIs, DevOps, and technical leadership.

Design and implement SAP Cloud Platform Integration solutions, particularly for SAP SuccessFactors Employee Central. Develop and manage end-to-end cloud integrations using web services, XML, JSON, REST, and OData. Support global projects, ensure data quality, apply .NET and DevOps practices, collaborate with international teams, and adapt solutions to changing business needs. Travel may be required for national and international assignments.

Lead AI engineering maturity assessments and advise enterprise CTOs, CIOs, and engineering leaders on AI-enabled SDLC and agentic systems. Build and prototype AI-assisted engineering workflows, multi-agent systems, and secure enterprise architectures. Translate diagnostics into actionable transformation roadmaps, capability heatmaps, governance models, and tooling recommendations. Facilitate executive workshops, publish thought leadership, contribute to AI frameworks and accelerators, and travel globally to work with customers.

Leads global infrastructure engineering teams and oversees the development, deployment, reliability, security, compliance, automation, observability, and cost optimization of enterprise infrastructure and cloud services. Establishes infrastructure product roadmaps, modernizes legacy systems, manages IT budgets, ensures service-level objectives, and collaborates with cybersecurity and other stakeholders to deliver scalable, reliable platforms.

Define and execute the technology vision, strategy, and roadmap; oversee architecture, data engineering, cloud platforms, security, scalability, and technology delivery. Partner with executive and commercial stakeholders to align technology with business outcomes, manage technical risks, and lead senior engineering and technical leadership teams in enterprise-scale environments.

Conduct data science work involving machine learning, NLP, graph databases, statistical analysis, predictive modeling, data management, and visualization. The role requires Python expertise, problem-solving, collaboration, adaptability, and innovative thinking. Travel may be required for national and international business activities.

Build and advise on AI-native engineering transformations for enterprise customers. Lead AI maturity assessments, evaluate AI use across engineering workflows and the SDLC, design agentic systems, and create actionable transformation roadmaps. Prototype secure AI architectures and workflows while advising CTOs, CIOs, and engineering leaders. Facilitate executive workshops, develop thought leadership, and contribute to AI frameworks, maturity indexes, and engineering accelerators.

Designs, develops, and deploys production Generative AI and LLM applications, including RAG pipelines, AI agents, semantic search, backend APIs, and enterprise data integrations. Builds scalable data and MLOps solutions using Databricks, Snowflake, and AWS. Collaborates across engineering and data teams while optimizing model performance, cost, latency, security, compliance, and privacy, including HIPAA requirements where applicable.