About Saarthee:
Saarthee is a Global Strategy, Analytics, Technology and AI consulting company, where our passion for helping others fuels our approach and our products and solutions. We are a onestop shop for all things data and analytics. Unlike other analytics consulting firms that are technology or platform specific, Saarthee’s holistic and tool agnostic approach is unique in the marketplace. Our Consulting Value Chain framework meets our customers where they are in their data journey. Our diverse and global team work with one objective in mind: Our Customers’ Success. At Saarthee, we are passionate about guiding organizations towards insights fueled success. That’s why we call ourselves Saarthee–inspired by the Sanskrit word ‘Saarthi’, which means charioteer, trusted guide, or companion. Cofounded in 2015 by Mrinal Prasad and Shikha Miglani, Saarthee already encompasses all the components of Data Analytics consulting. Saarthee is based out of Philadelphia, USA with office in UK and India
Position Summary:
We are looking for a highly capable Enterprise Lead / Enterprise Architect to help shape and scale an AI-powered analytics and decision intelligence platform. This role sits at the intersection of enterprise architecture, AI orchestration, analytics engineering, platform design, customer solutioning, and scalable productization.
The ideal candidate can think strategically while also driving execution across architecture, engineering standards, integration frameworks, data flows, security, and enterprise-scale deployment patterns. This is not a traditional IT architect role – this role is for someone who can help build the future of enterprise AI systems.
Responsibilities:
Enterprise Architecture & Platform Design
-
Define scalable enterprise architecture patterns for AI and analytics platforms.
-
Design modular, reusable, and configurable platform capabilities.
-
Establish standards for APIs, orchestration, semantic layers, workflow engines, integration patterns, and security frameworks.
-
Drive platform-first engineering principles to reduce customization overhead.
-
Define multi-tenant and enterprise deployment models (SaaS, Hybrid, On-Prem).
AI & Data Systems Architecture
-
Architect AI-native workflows using LLM orchestration, RAG, agentic frameworks, semantic retrieval, vector architectures, and enterprise context grounding.
-
Define scalable data flow and transformation patterns.
-
Build reusable frameworks for profiling, data quality, transformation, insight generation, forecasting, and anomaly detection.
-
Ensure explainability, traceability, and auditability of AI outputs.
Engineering Leadership
-
Provide technical leadership across product and engineering teams.
-
Establish coding, deployment, and architecture standards.
-
Drive engineering scalability and maintainability.
-
Review architecture decisions and prevent technical debt accumulation.
-
Mentor AI-native full stack engineers and platform teams.
-
Lead architectural reviews and solution governance.
Enterprise Integration & Security
-
Design enterprise-grade integrations with cloud platforms, CRMs, data warehouses, BI systems, ERP platforms, and contact center systems.
-
Define security and governance standards including RBAC, SSO/MFA, encryption, tokenization, data isolation, and AI guardrails.
-
Support compliance alignment: SOC2, ISO27001, GDPR, and HIPAA (where applicable).
Product & Customer Collaboration
-
Partner with founders and product leadership to translate business vision into scalable architecture.
-
Participate in enterprise discovery and solution workshops.
-
Guide customers on AI transformation roadmaps and platform adoption.
-
Help shape reusable vertical templates and enterprise deployment models.
-
Balance customer needs with platform standardization.
Requirement:
-
10+ years of experience in enterprise architecture, platform engineering, analytics, or AI systems.
-
Strong experience designing scalable enterprise applications and distributed systems.
-
Hands-on experience with cloud-native architectures (AWS / Azure / GCP).
-
Experience with APIs and microservices, orchestration frameworks, data platforms, AI/LLM systems, and enterprise integrations.
-
Strong understanding of analytics ecosystems, semantic data modeling, workflow automation, and AI operationalization.
-
Experience building or scaling enterprise SaaS or AI platforms.
-
Ability to communicate effectively with both technical teams and executive stakeholders.
-
College or University degree in Computer Science, Engineering, or a related field.
Core Competency Requirements:
-
Expertise in designing modular, reusable, and configurable enterprise platform capabilities.
-
Deep knowledge of AI/LLM orchestration frameworks and agentic system architectures.
-
Hands-on experience with RAG pipelines, vector databases, and semantic retrieval architectures.
-
Strong understanding of enterprise security standards: RBAC, SSO/MFA, encryption, and data isolation.
-
Experience with multi-tenant and hybrid/on-prem enterprise deployment models.
-
Proficiency with CI/CD, source control systems, and scalable software delivery practices.
-
Strong analytical and problem-solving skills with the ability to simplify complex systems.
-
Internally motivated; able to work proficiently both independently and in a team environment.
-
Strong communication skills with internal team members and external business stakeholders.
Preferred Competency Requirements:
-
Exposure to agentic systems, enterprise copilots, and AI decision intelligence platforms.
-
Experience in telecom, financial services, healthcare, insurance, or large enterprise analytics environments.
-
Understanding of data governance and enterprise AI risk management frameworks.
-
Experience building configurable multi-client platforms.
-
Startup or high-growth product experience preferred.
-
Familiarity with compliance frameworks: SOC2, ISO27001, GDPR, and HIPAA.
