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EXL

Analyzes business data and runs outsourced operations

Solution Architect - Data & AI Solutions

Pre-Sales Solution EngineeringConfirmed live in the last 24 hours
Full-timeBengaluru, Karnataka, IndiaNo salary listedIn-person

About the Job

Requirements

  • A Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
  • At least 15 years of experience in software architecture, presales engineering, or enterprise data and AI platform design.
  • Hands-on expertise in at least two hyperscaler platforms: Azure (Fabric, Synapse, Data Factory, Azure Machine Learning, Power BI), AWS (Redshift, Glue, S3, SageMaker, Lake Formation), or 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 experience with observability stacks such as Prometheus, Grafana, OpenTelemetry, or ELK.
  • Advanced programming experience with Python (async), TypeScript/JavaScript, or Go.
  • Familiarity with Kubernetes, service mesh such as Istio, and serverless design patterns.
  • Experience with CI/CD automation, GitOps, Terraform, and Helm.
  • Strong presentation, storytelling, and client engagement skills.
  • Strong hands-on experience with at least one hyperscaler: AWS, Azure, or GCP.
  • Deep expertise in PySpark, SQL, Python, Databricks or Snowflake (one is mandatory and both are preferred), cloud data services such as Kinesis, Glue, Redshift, Synapse, BigQuery, or DataProc, Kubernetes, Docker, CI/CD, IAM, VPC, private networking, secrets, and API management.
  • Demonstrated ability to work directly with client engineering teams.
  • Ability to run design discussions, debugging sessions, and deployment workshops.
  • Ability to simplify technical topics for business audiences.
  • Ability to work independently with a consulting mindset and ownership mentality.

Responsibilities

  • Partner with sales and industry teams to design client-ready demos, proof-of-concepts, and technical solution blueprints.
  • Present complex architectures clearly and in a business-outcome-driven manner to executive and technical stakeholders.
  • Contribute to RFP/RFI responses, solution proposals, and deal shaping.
  • Advise clients and highlight differentiators of Data & AI Solutions.
  • 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 such as Kafka, Pulsar, or SQS, and state machines.
  • Build reusable, industry-specific demo environments using hyperscaler services and data platforms.
  • Define and enforce observability standards for demo and enterprise environments, including logging, tracing, telemetry, and real-time alerting.
  • Implement zero-trust security models using RBAC/ABAC, IAM, OAuth2, encryption, and API gateways.
  • Ensure demo and client environments meet compliance standards such as HIPAA, GDPR, and SOC 2.
  • Work with Product, Data Science, Engineering, and Governance teams to align demos with business and regulatory needs.
  • Collaborate with vertical teams to build domain-specific demo templates, such as insurance claims, healthcare payment integrity, banking KYC/fraud, and retail personalization.
  • Provide hands-on support to engineering teams during delivery, troubleshooting, and performance tuning.
  • Keep current with hyperscaler advancements, AI/ML frameworks, and orchestration patterns.
  • Drive technical due diligence, proof-of-concepts, and vendor/platform evaluations.
  • Create technical artifacts, including architecture diagrams, design patterns, and runbooks.
  • Mentor presales engineers and junior architects in solution design and client presentation skills.

Desired Qualifications

  • Cloud certifications in AWS, Azure, or GCP.
  • Certifications in Kubernetes or the CNCF ecosystem.
  • Knowledge of architecture frameworks such as TOGAF or SAFe.
  • Experience with multi-tenant SaaS platforms and usage-based billing.
  • Familiarity with data mesh, knowledge graphs, and semantic interoperability.
  • Knowledge of frontend architecture patterns such as micro-frontends and data visualizations.
  • Experience building presales demo or sandbox environments.
  • Exposure to agentic AI concepts and LLM-based orchestration.
  • Exposure to LLMs or agent tooling such as LangChain, LangGraph, or CrewAI, or willingness to learn quickly.
  • Strong interest in how AI can automate data engineering and governance.

About the Company

EXL

Analyzes business data and runs outsourced operations

EXL analyzes business data and runs outsourced operations for insurers, healthcare payers, banks, retailers, and other large organizations. Its teams combine industry expertise with data engineering and AI to handle tasks such as claims processing, payment reviews, customer service, and finance within existing technology environments. The company also builds software, including LifePRO, which manages insurance policies, and EXLdata.ai, which connects fragmented data for use in AI systems. Clients pay for consulting projects, ongoing managed services, and software delivered through the cloud. EXL was founded in 1999 and is headquartered in New York City.

Company size
10,001+
Company stage
IPO
Headquarters
New York City, New York
Founded
1999

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