Solution Architect – Data & AI Solutions at EXL Talent Acquisition Team

, , India -

Full Time

Start Date

Immediate

Expiry Date

14 Oct, 26

Salary

0.0

Posted On

16 Jul, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Solution Architecture, Presales Engineering, Azure, AWS, GCP, Distributed Systems, AI/ML Platforms, Event-Driven Architecture, Python, Kubernetes, Data Governance, CI/CD, Observability, Zero-Trust Security, Client Engagement, Multi-model AI Orchestration

Industry

Business Consulting and Services

Description

Key Responsibilities: Presales & Client Engagement * Partner with sales and industry teams to design client-ready demos, proof-of-concepts, and technical solution blueprints. * Present complex architectures in a clear, business-outcome-driven manner to both executive and technical stakeholders. * Contribute to RFP/RFI responses, solution proposals, and deal shaping. * Act as a trusted advisor in client conversations, highlighting differentiators of Data & AI Solutions. Solution Architecture & Demo Environments * Architect distributed, scalable, and resilient data and AI platforms for presales demonstrations. * Design abstraction layers for multi-model AI orchestration, including fallback logic, dynamic model switching, and cost control. * Lead implementation of event-driven architectures using messaging frameworks (Kafka, Pulsar, SQS, etc.) and state machines. * Build reusable, industry-specific demo environments leveraging hyperscaler services and data platforms. Observability, Security & Compliance * Define and enforce observability standards for demo and enterprise environments (logging, tracing, telemetry, real-time alerting). * Implement zero-trust security models (RBAC/ABAC, IAM, OAuth2, encryption, API gateways). * Ensure all demo and client environments meet compliance standards such as HIPAA, GDPR, SOC2. Cross-Functional Collaboration * Work with Product, Data Science, Engineering, and Governance teams to align demos with business/regulatory needs. * Collaborate with IMUs (verticals) to build domain-specific demo templates (e.g., Insurance claims, Healthcare payment integrity, Banking KYC/fraud, Retail personalization). * Provide hands-on support to engineering teams during delivery, troubleshooting, and performance tuning. Innovation & Technical Leadership * Stay ahead of hyperscaler advancements, AI/ML frameworks, and orchestration patterns. * Drive technical due diligence, PoCs, and vendor/platform evaluations. * Create technical artifacts (architecture diagrams, design patterns, runbooks). * Mentor presales engineers and junior architects in solution design and client presentation skills. Qualifications: * Bachelor’s or Master’s degree in Computer Science, Engineering, or related field. * Certifications preferred: * Cloud (AWS, Azure, GCP). * Kubernetes / CNCF ecosystem. * Architecture frameworks (TOGAF, SAFe). * Skills & Experience Must-Have Skills & Experience * 15+ years in software architecture, presales engineering, or enterprise data/AI platform design. * Hands-on expertise in at least two hyperscaler platforms: * Azure (Fabric, Synapse, Data Factory, Azure ML, Power BI). * AWS (Redshift, Glue, S3, SageMaker, Lake Formation). * GCP (BigQuery, Dataplex, Vertex AI, Pub/Sub). * Proven experience architecting distributed systems, microservices, and scalable AI/ML platforms. * Strong knowledge of Data Governance, Data Quality, Metadata, Lineage, and DataOps. * Expertise in event-driven systems and asynchronous workflows. * Hands-on with observability stacks (Prometheus, Grafana, OpenTelemetry, ELK). * Advanced programming with Python (async), TypeScript/JavaScript, or Go. * Familiarity with Kubernetes, service mesh (Istio), serverless design patterns. * Experience with CI/CD automation, GitOps, Terraform, Helm. * Strong presentation, storytelling, and client engagement skills. Preferred Skills * Experience with multi-tenant SaaS platforms and usage-based billing. * Familiarity with data mesh, knowledge graphs, and semantic interoperability. * Knowledge of frontend architecture patterns (micro-frontends, data visualizations). * Experience building presales demo or sandbox environments. Exposure to agentic AI concepts and LLM-based orchestration.Presales & Client Engagement * Partner with sales and industry teams to design client-ready demos, proof-of-concepts, and technical solution blueprints. * Present complex architectures in a clear, business-outcome-driven manner to both executive and technical stakeholders. * Contribute to RFP/RFI responses, solution proposals, and deal shaping. * Act as a trusted advisor in client conversations, highlighting differentiators of Data & AI Solutions. Solution Architecture & Demo Environments * Architect distributed, scalable, and resilient data and AI platforms for presales demonstrations. * Design abstraction layers for multi-model AI orchestration, including fallback logic, dynamic model switching, and cost control. * Lead implementation of event-driven architectures using messaging frameworks (Kafka, Pulsar, SQS, etc.) and state machines. * Build reusable, industry-specific demo environments leveraging hyperscaler services and data platforms. Observability, Security & Compliance * Define and enforce observability standards for demo and enterprise environments (logging, tracing, telemetry, real-time alerting). * Implement zero-trust security models (RBAC/ABAC, IAM, OAuth2, encryption, API gateways). * Ensure all demo and client environments meet compliance standards such as HIPAA, GDPR, SOC2. Cross-Functional Collaboration * Work with Product, Data Science, Engineering, and Governance teams to align demos with business/regulatory needs. * Collaborate with IMUs (verticals) to build domain-specific demo templates (e.g., Insurance claims, Healthcare payment integrity, Banking KYC/fraud, Retail personalization). * Provide hands-on support to engineering teams during delivery, troubleshooting, and performance tuning. Innovation & Technical Leadership * Stay ahead of hyperscaler advancements, AI/ML frameworks, and orchestration patterns. * Drive technical due diligence, PoCs, and vendor/platform evaluations. * Create technical artifacts (architecture diagrams, design patterns, runbooks). * Mentor presales engineers and junior architects in solution design and client presentation skills. Qualifications: * Bachelor’s or Master’s degree in Computer Science, Engineering, or related field. * Certifications preferred: * Cloud (AWS, Azure, GCP). * Kubernetes / CNCF ecosystem. * Architecture frameworks (TOGAF, SAFe). * Skills & Experience Must-Have Skills & Experience * 15+ years in software architecture, presales engineering, or enterprise data/AI platform design. * Hands-on expertise in at least two hyperscaler platforms: * Azure (Fabric, Synapse, Data Factory, Azure ML, Power BI). * AWS (Redshift, Glue, S3, SageMaker, Lake Formation). * GCP (BigQuery, Dataplex, Vertex AI, Pub/Sub). * Proven experience architecting distributed systems, microservices, and scalable AI/ML platforms. * Strong knowledge of Data Governance, Data Quality, Metadata, Lineage, and DataOps. * Expertise in event-driven systems and asynchronous workflows. * Hands-on with observability stacks (Prometheus, Grafana, OpenTelemetry, ELK). * Advanced programming with Python (async), TypeScript/JavaScript, or Go. * Familiarity with Kubernetes, service mesh (Istio), serverless design patterns. * Experience with CI/CD automation, GitOps, Terraform, Helm. * Strong presentation, storytelling, and client engagement skills. Preferred Skills * Experience with multi-tenant SaaS platforms and usage-based billing. * Familiarity with data mesh, knowledge graphs, and semantic interoperability. * Knowledge of frontend architecture patterns (micro-frontends, data visualizations). * Experience building presales demo or sandbox environments. Exposure to agentic AI concepts and LLM-based orchestration.

Responsibilities

Design and architect scalable Data and AI platforms for presales demonstrations and proof-of-concepts. Act as a technical advisor to clients, bridging the gap between business outcomes and complex technical architectures.