Top Tech Jobs & Startup Jobs

Lead development of production agentic AI applications, translating business outcomes into agent specifications, tools, evaluations, and acceptance criteria. Build retrieval, orchestration, structured-output, approval, fallback, and failure-handling workflows using LLMs and APIs. Own evaluation harnesses, lead agile ceremonies, estimate work, and create sprint plans. Develop applications with Python or Java/Spring Boot and modern front-end technologies, while providing technical leadership and client-ready communication.

Lead the vision, strategy, roadmap, and delivery of an enterprise AML monitoring data platform. Own transaction and party data models, detection rules, alert and case data, risk scoring, governance, quality, analytics, and AI/ML use cases. Partner with compliance, investigations, risk, data engineering, architecture, governance, and analytics teams to define requirements, manage Agile delivery, establish metrics, and improve detection performance, adoption, and business impact.

Build and maintain CI/CD pipelines, implement Terraform-based Infrastructure as Code, and support Kubernetes container orchestration for cloud-native data solutions. Collaborate with cloud and data engineering teams, provide production support and incident response, and improve monitoring and observability. Preferred experience includes GitOps tools such as ArgoCD or Flux, Kubernetes cluster management, DevSecOps, compliance automation, and FinOps.

Build and maintain CI/CD pipelines, implement Infrastructure as Code with Terraform, support Kubernetes and cloud platforms, respond to production incidents, and improve monitoring and observability. Collaborate with Cloud Solutions and Data Engineering teams to enable secure, repeatable delivery of cloud-native data solutions. Experience with GitOps, Kubernetes cluster management, DevSecOps, compliance automation, and FinOps is advantageous.

Lead Snowflake architecture, administration, governance, performance and cost optimization across enterprise data warehouse environments. Implement RBAC and compliance controls, design replication and disaster recovery strategies, and manage production incident response. The role requires extensive data platform or database engineering experience, Snowflake production administration, high-availability or disaster recovery expertise, cloud data platform experience, and familiarity with Databricks, ETL platforms, Infrastructure as Code, and regulated environments.

Supports enterprise data, migration, integration, and modernization programs by gathering requirements, profiling and validating data with SQL, creating source-to-target mappings, documenting transformation rules, coordinating UAT, and tracing issues across systems. Partners with business stakeholders, Data Engineering, QA, and BI teams to drive validation, sign-off, reporting, and resolution of complex data issues.

Design, develop, and deploy scalable cloud-based data pipelines and ETL/ELT processes. Build and optimize Snowflake and dbt solutions, apply data warehousing concepts such as dimensional modeling, CDC, and SCDs, and use workflow orchestration tools. Ensure data governance, security, and privacy best practices while collaborating with technical and non-technical stakeholders in an Agile environment.

Lead end-to-end delivery of scalable AI and GenAI applications, owning architecture, performance, agentic workflows, tool orchestration, and RAG systems. Review prompts, code, and application architecture; mentor AI engineers; conduct technical reviews; and collaborate with stakeholders to translate business needs into production solutions. Drive best practices for LLMOps, observability, reliability, and cloud-native design.

Build and maintain CI/CD pipelines, implement Infrastructure as Code with Terraform, support Kubernetes and cloud platforms, and collaborate with cloud and data engineering teams. The role includes production support, incident response, monitoring and observability improvements, and secure, repeatable delivery of cloud-native data solutions. Additional responsibilities may include GitOps, Kubernetes cluster management, DevSecOps automation, compliance, and cloud cost optimization.

Owns end-to-end delivery of AI-powered automation, intelligent agents, copilots, and LLM workflows. Engages stakeholders, gathers requirements, designs architectures, evaluates feasibility and ROI, develops Python and Azure-based solutions, and manages testing, deployment, monitoring, and support. The role uses Azure OpenAI, LangChain, LangGraph, RAG, embeddings, APIs, microservices, and cloud data platforms while leading demos, UAT, delivery timelines, and continuous innovation.