Solution Architect

Luxoft; Gurugram District; On-site; INR 3,500,000 - 7,000,000; Full time

Job summary

Luxoft is seeking a seasoned Solution Architect to design and govern scalable software solutions across Capital Markets analytics platforms. The role requires expertise in client-server, AWS, AI infrastructure, and responsible AI governance aligned with business strategy and security standards.

Responsibilities include architecture design, cloud migration strategies, AI/ML infrastructure, and governance across data, AI, and platforms.

Qualifications

• 8-12+ years in software engineering and architecture roles.
• Proven experience designing large-scale distributed systems.
• Knowledge of .NET and/or Python.
• Strong knowledge of microservices, APIs, and AWS.

Responsibilities

• Design end-to-end solution architectures spanning microservices, APIs and domain platforms.
• Define architecture patterns, standards, and reusable frameworks.
• Translate business requirements into scalable, secure technical solutions.
• Architect cloud-native and hybrid solutions on AWS with high availability and cost optimization.

Skills

Core Architecture

Distributed Systems

Python / .NET

Microservices

APIs & Integrations

AWS & Cloud

AI / ML & LLM

Observability Tools

Security & IAM

Leadership & Mentorship

Tools

EC2

Lambda

ECS/EKS

VPC

Route 53

API Gateway

S3

RDS/Aurora

CloudFormation/CDK/Terraform

OpenTelemetry

Langfuse

Prometheus/Grafana

SageMaker

Job description

Project description

We are seeking a highly experienced Solution Architect to design, guide, and govern scalable software solutions across the Capital Markets analytics platforms.This role requires strong expertise in traditional client-server architecture, 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 solution architectures spanning:
  • Component-level services (microservices, APIs)
  • Domain platforms
• 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 platformsCloud 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:
  • RAG (Retrieval-Augmented Generation)
  • Feature stores and data pipelines
  • Model deployment, versioning, and scaling
• AI Governance, Observability & Control
• Define and implement enterprise AI governance frameworks covering:
  • Responsible AI usage (fairness, bias mitigation, explainability)
  • Data privacy, lineage, and compliance
  • AI risk classification and policy enforcement
• Establish AI observability and monitoring capabilities, including:
  • End-to-end tracing of AI/ML and LLM flows using tools such as OpenTelemetry
  • Monitoring of prompts, responses, latency, and model behavior using platforms like Langfuse or equivalento Metrics for model performance, drift, hallucination rates, and usage patterns
• Design and enforce agent governance and control mechanisms, including:
  • Monitoring and auditing of autonomous and semi-autonomous AI agents
  • Guardrails for agent behavior, tool usage, and decision boundaries
  • Human-in-the-loop (HITL) workflows and escalation patterns
  • Policy-based control over agent actions and integrations
• Implement AI lifecycle governance, including:
  • Model validation, approval workflows, and audit trail
  • Continuous evaluation and feedback loops
  • 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
• Knowledge of .NET and/or Python
• Strong knowledge of:
  • Microservices and event-driven architectures
  • API management and integrationsAWS TechnologiesCompute & Containers
• Amazon EC2, AWS Lambda
• Amazon ECS / EKS (Kubernetes)Networking & Integration
• Amazon VPC, Route 53, API Gateway
• AWS App Mesh, EventBridge, SNS, SQSData & Storage
• Amazon S3, EBS, Glacier
• Amazon RDS, Aurora, DynamoDB, Redshift
• DevOps & Automation
  • AWS CloudFormation / CDK / Terraform
  • AWS CodePipeline, CodeBuild, CodeDeployObservability
  • Amazon CloudWatch, AWS X-RaySecurity
  • AWS IAM, Cognito, KMS, Secrets Manager
  • AWS Organizations and Control Tower
• AI/ML, LLM & Observability Expertise
  • Experience with AWS AI/ML stack:
    • Amazon SageMaker
    • Amazon Bedrock (LLMs & foundation models)
    • AWS Glue, Lake Formation
  • Hands-on experience with:
    • LLM-based architectures and agent-based systems
    • AI observability tools (e.g., OpenTelemetry, Langfuse, Prometheus/Grafana)
    • Prompt lifecycle management and evaluation pipelines
  • Strong understanding of:
    • AI governance frameworks and enterprise AI control
    • Agent orchestration, monitoring, and guardrails
    • Data lineage, quality, and complianceArchitecture Frameworks & Practices
• TOGAF or equivalent enterprise architecture frameworks
• Domain-driven design (DDD)
• Cloud-native and serverless patterns
• Experience integrating data, semantic, and ML architecturesSoft 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 AI governance frameworks
• Background in regulated industries
• Exposure to multi-cloud or hybrid environments