Jobs at Satori Analytics

Design, develop, and operate AI workflows and models for marketing products in payments and financial services. Build scalable Python and LLM-based solutions, collaborate with data scientists, engineers, and business stakeholders, research emerging AI techniques, translate business needs into ML solutions, communicate results, and document work for reproducibility.

Owns a portfolio of enterprise accounts, driving revenue, margin, client satisfaction, delivery quality, and account growth. Builds executive relationships, leads account planning, proposals, solution shaping, negotiations, and delivery governance. Translates business needs into data and AI solutions while guiding technical teams and multidisciplinary consultants. Requires extensive hands-on data and AI delivery experience, consulting background, technical literacy, senior client relationship management, and leadership of complex programs.

Develop and test high-quality Python software, improve code performance and reusability, collaborate with architects, data engineers, and clients, participate in Agile ceremonies, and document cloud-based solutions. The role requires Python development with web frameworks, front-end experience, cloud technologies, and RESTful APIs.

Own the day-to-day product lifecycle for enterprise AI systems, translating strategy into roadmaps, epics, and user stories. Manage prioritization, delivery, releases, trials, onboarding, deployments, marketplaces, SLAs, and customer success. Support pricing, competitor analysis, business cases, and sales enablement while aligning internal AI research with product priorities. The role requires enterprise product experience, technical fluency, AI literacy, strong writing, and a process-building mindset.

Leads AI strategy consulting engagements for enterprise clients, advising executives on AI value creation, business cases, investment prioritization, KPIs, operating models, governance, and organizational impact. Owns the practice methodology, quality standards, thought leadership, offerings, and colleague coaching. Supports proposals and vendor evaluations while assessing AI feasibility and delivery outcomes. Requires top-tier consulting experience, enterprise advisory expertise, executive presence, AI fluency, quantitative rigor, and fluency in English and Greek.

Design, build, and operate scalable batch, streaming, and real-time data platforms for marketing analytics and AI/ML products. Develop ingestion, transformation, orchestration, and delivery frameworks across cloud data environments; improve scalability, reliability, security, governance, observability, and lineage. Lead cloud modernization, CI/CD and infrastructure automation, resolve complex production issues, influence architecture, mentor engineers, and collaborate with product, engineering, data science, and business stakeholders.

Design and develop scalable AI workflows and models in Python, focusing on LLMs and machine learning. Collaborate with data scientists, developers, and domain experts to translate business needs into practical solutions. Research emerging AI techniques, build and deploy models, document reproducible work, and communicate results clearly. The role requires experience with ML frameworks, LLM applications, vector databases, cloud platforms, APIs, and software development practices.

Architects and builds production-grade GenAI systems, including RAG pipelines, LLM integrations, agent workflows, evaluation frameworks, guardrails, and monitoring. Optimizes inference cost, latency, reliability, and quality while developing reusable AI components and services. Leads the transition from prototypes to production, mentors junior engineers, and manages one to two engineers.

Lead and mentor Cloud Engineers while owning the technical direction, roadmap, and delivery of cloud and platform infrastructure. Architect and operate Azure, Kubernetes, hybrid, and on-premises environments supporting production, data, and AI workloads. Drive Terraform-based infrastructure automation, GitOps, CI/CD, observability, security, resilience, and incident response. Collaborate with Software, Data, and AI Engineering teams on scalable infrastructure, including GPU and data-intensive workloads.