Satori Analytics

Solutions Engineer

• **What Your Day Might Look Like:**
• - Lead technical pre-sales engagements across Data Science, GenAI, and Data Estate transformation initiatives.
• - Conduct discovery workshops to uncover business objectives, technical constraints, risks, and success criteria.
• - Contribute in designing scalable, secure, and production-ready end-to-end Data & AI architectures, from ingestion and transformation to deployment and governance.
• - Contribute in architecting GenAI solutions (RAG pipelines, LLM orchestration, agent frameworks, evaluation mechanisms, guardrails).
• - Contribute in defining enterprise data platform architectures (data lakes, lakehouses, warehouses, ETL/ELT pipelines, analytics layers).
• - Translate business challenges into clear technical proposals, solution diagrams, SoWs, and effort estimations.
• - Support RFP/RFI responses with clarity, structure, and technical depth.
• - Collaborate closely with delivery teams to ensure proposed architectures are realistic, scalable, and implementable.
• - Contribute to reusable accelerators, reference architectures, and internal best practices.
• - Act as the trusted technical advisor to executive and technical stakeholders alike.

AI Solution Architect

Data Scientist

• **What Your Day Might Look Like:**
• - **Lead end-to-end AI delivery**: Own ML initiatives from ideation to production — from forecasting and predictive models to recommender systems and explainable AI.
• - **Design for scale**: Shape robust ML architectures (data pipelines, feature stores, serving layers) that are production-ready and maintainable.
• - **Grow your team**: Manage and mentor Data Scientists and AI Engineers, fostering ownership, high standards, and continuous development.
• - **Bridge business & tech**: Partner with commercial and product teams to identify AI opportunities and contribute to pre-sales discussions when needed.
• - **Drive best practices**: Establish scalable ML standards, ensuring quality, reproducibility, and responsible AI development.
• - **Turn complexity into clarity**: Translate advanced ML concepts into actionable insights for business and executive stakeholders.
• - **Push innovation forward**: Bring applied research into real-world solutions that create measurable impact.

Data Science Manager

Business Analyst

• Understand the business: Work closely with client stakeholders to understand context, goals, and pain points — and challenge assumptions when needed.
• Translate into action: Turn business requirements into clear user stories and specifications that technical teams can implement.
• Align teams: Collaborate with Solution Architects, Developers, Data Engineers, and other stakeholders to ensure clarity and feasibility.
• Drive delivery: Support project planning, track progress, identify risks, and help ensure projects are delivered on time and on budget.
• Communicate clearly: Keep clients informed about progress, challenges, and next steps — even when things get complex.
• Map the landscape: Analyze current processes, data flows, and system architecture to identify gaps and opportunities.
• Measure success: Define KPIs and success criteria, track impact, and report results.
• Lead agile routines: Participate in (and when needed, facilitate) stand-ups, sprint planning, and retrospectives.
• Support growth: Contribute to documentation, training material, and knowledge sharing across the team.

AI Engineer

• Architect and build scalable, production-grade GenAI systems and services.
• Design and implement Retrieval-Augmented Generation (RAG) pipelines end-to-end.
• Integrate and orchestrate LLMs (OpenAI, Anthropic, Google, or open-source models) in real-world applications.
• Build internal abstractions, SDKs, and reusable components for GenAI capabilities.
• Implement evaluation pipelines, guardrails, and monitoring for hallucination detection, drift, and quality control.
• Optimize inference performance, cost, token usage, and response latency.
• Design multi-step agent workflows and tool integrations safely and reliably.
• Lead the transition from experimentation to hardened production systems.
• Mentor engineers and establish best practices for GenAI engineering.
• Line management responsibilities – 1-2 bright junior engineers.

AI Solution Architect

Data Science Manager

Business Analyst

Senior AI Engineer