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Luxoft

Solution Architect

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Project description

We are seeking a highly experienced Enterprise Architect to design, guide, and govern scalable software solutions across the organization—ranging from individual components to fully integrated enterprise platforms. This role requires strong expertise in AWS cloud technologies, AI infrastructure, and advanced AI governance practices, ensuring solutions align with business strategy, security standards, and responsible AI policies.

Responsibilities

  • Architecture & Solution Design
  • Design end-to-end architectures spanning: o Component-level services (microservices, APIs) o Domain platforms o Enterprise-wide ecosystems
  • Define architecture patterns, standards, and reusable frameworks
  • Translate business requirements into scalable and secure technical solutions
  • Ensure interoperability across systems, data layers, AI services, and platforms Enterprise Architecture Strategy
  • Develop and maintain enterprise architecture roadmaps
  • Align IT strategy with business goals and digital transformation initiatives
  • Establish governance models (TOGAF/SAFe or similar)
  • Lead architecture review boards and technical decision-making processes Cloud Architecture (AWS)
  • Architect and optimize cloud-native and hybrid solutions using AWS services
  • Define cloud migration strategies and modernization approaches
  • Ensure high availability, resiliency, cost optimization, and performance
  • Implement Infrastructure-as-Code and automation best practices AI, Data & Intelligent Systems Architecture
  • Design AI/ML infrastructure, pipelines, and enterprise integration patterns
  • Architect solutions incorporating LLMs, generative AI, and intelligent agents
  • Guide adoption of AI technologies within enterprise platforms and products
  • Establish patterns for: o RAG (Retrieval-Augmented Generation) o Feature stores and data pipelines o Model deployment, versioning, and scaling AI Governance, Observability & Control
  • Define and implement enterprise AI governance frameworks covering: o Responsible AI usage (fairness, bias mitigation, explainability) o Data privacy, lineage, and compliance o AI risk classification and policy enforcement
  • Establish AI observability and monitoring capabilities, including: o End-to-end tracing of AI/ML and LLM flows using tools such as OpenTelemetry o Monitoring of prompts, responses, latency, and model behavior using platforms like Langfuse or equivalent o Metrics for model performance, drift, hallucination rates, and usage patterns
  • Design and enforce agent governance and control mechanisms, including: o Monitoring and auditing of autonomous and semi-autonomous AI agents o Guardrails for agent behavior, tool usage, and decision boundaries o Human-in-the-loop (HITL) workflows and escalation patterns o Policy-based control over agent actions and integrations
  • Implement AI lifecycle governance, including: o Model validation, approval workflows, and audit trails o Continuous evaluation and feedback loops o Secure model and prompt management Cross-Disciplinary Architecture Leadership
  • Act as a strategic liaison across Semantic, Data, and ML architecture domains
  • Facilitate alignment between knowledge graphs, ontologies, data platforms, and ML systems
  • Provide architectural guidance to specialized architects, ensuring cohesive enterprise integration
  • Bridge gaps between business semantics, data engineering, and machine learning pipelines Security, Compliance & Governance
  • Ensure architectures meet enterprise security standards (e.g., Zero Trust)
  • Define policies for data governance, access control, and auditability
  • Align AI and cloud solutions with regulatory and compliance frameworks Collaboration & Leadership
  • Work with engineering, product, data, and AI teams to align solutions
  • Mentor architects and senior engineers
  • Act as a trusted advisor to leadership and stakeholders

SKILLS

Must have

  • Core Architecture
  • 8-12+ years in software engineering and architecture roles
  • Proven experience designing large-scale distributed systems
  • Strong knowledge of: o Microservices and event-driven architectures o API management and integrations o Enterprise integration patterns (EIPs) AWS Technologies Compute & Containers
  • Amazon EC2, AWS Lambda
  • Amazon ECS / EKS (Kubernetes) Networking & Integration
  • Amazon VPC, Route 53, API Gateway
  • AWS App Mesh, EventBridge, SNS, SQS Data & Storage
  • Amazon S3, EBS, Glacier
  • Amazon RDS, Aurora, DynamoDB, Redshift DevOps & Automation
  • AWS CloudFormation / CDK / Terraform
  • AWS CodePipeline, CodeBuild, CodeDeploy Observability
  • Amazon CloudWatch, AWS X-Ray Security
  • AWS IAM, Cognito, KMS, Secrets Manager
  • AWS Organizations and Control Tower AI/ML, LLM & Observability Expertise
  • Experience with AWS AI/ML stack: o Amazon SageMaker o Amazon Bedrock (LLMs & foundation models) o AWS Glue, Lake Formation
  • Hands-on experience with: o LLM-based architectures and agent-based systems o AI observability tools (e.g., OpenTelemetry, Langfuse, Prometheus/Grafana) o Prompt lifecycle management and evaluation pipelines
  • Strong understanding of: o AI governance frameworks and enterprise AI controls o Agent orchestration, monitoring, and guardrails o Data lineage, quality, and compliance Architecture Frameworks & Practices
  • TOGAF or equivalent enterprise architecture frameworks
  • Domain-driven design (DDD)
  • Cloud-native and serverless patterns
  • Experience integrating data, semantic, and ML architectures Soft Skills
  • Strong communication and stakeholder management
  • Strategic thinking with hands-on technical depth
  • Ability to influence senior leadership and cross-functional teams
  • Mentorship and leadership capabilities

Nice to have

• AWS Certified Solutions Architect - Professional • AWS Specialty Certifications (Machine Learning, Security) • Experience implementing enterprise AI governance frameworks • Background in regulated industries • Exposure to multi-cloud or hybrid environments