Solution Architect

Job Description

We are seeking an experienced Technical Lead / Solution Architect to design anddeliver enterprise-grade, multi-cloud and AI-enabled solutions. The role will provide technical leadership across application, data, integration, security, cloud, and observability, with a strong focus on Generative AI, Agentic AI,RAG, and LLMOps.

The successful candidate will lead client engagements, architecture design,proof of concepts, and technical delivery across AWS, Azure, and GCPenvironments.

Key Responsibilities
Translate business requirements into scalable GenAI and Agentic AI solution architectures, including RAG and AI agent-based solutions.
Own end-to-end architecture across applications, data, integrations, security, infrastructure, and observability.
Provide technical leadership for client opportunities, solution discussions, and implementation engagements.
Lead the design and development of POCs and MVPs, guiding engineeringteams through build, deployment, and operationalization.
Establish and implement LLMOps practices, including model evaluation, tracing, observability, guardrails, prompt management, versioning, and quality metrics.
Design batch and streaming data architectures, vector search capabilities,APIs, events, and agent/tool integration contracts.
Evaluate and recommend appropriate cloud platforms, AI models, managedservices, and open-source technologies based on cost, performance, scalability,and security.
Design secure solutions using Zero Trust, IAM, OAuth2/OIDC, secrets management,KMS, data classification, and access controls.
Define APIs and integration architectures using REST, gRPC, GraphQL, andevent-driven patterns.
Lead architecture reviews, design reviews, code reviews, and technicalgovernance activities.
Plan technical roadmaps, delivery backlogs, estimates, dependencies, and implementation strategies across multidisciplinary teams.
Work closely with clients and stakeholders to communicate technical decisions, risks, benefits, cost considerations, and ROI.
Coach and mentor engineering teams and establish reusable architecture patterns, templates, and reference implementations.
Manage multiple client opportunities and technical initiatives while maintaining strong stakeholder relationships.
Technical Requirements

• Strong hands-on experience across AWS, Azure, and GCP.
• Minimum 3 years of hands-on experience in each of AWS, Azure, and GCPenvironments.
• At least 1 year of hands-on experience with Generative AI and Agentic AItechnologies.
• Experience designing and delivering production-grade RAG and AI agentsolutions.
• Hands-on experience with at least one AI/agent framework, such as:

LangChain / LangGraph
DSPy
OpenAI or Anthropic tool use
Databricks Agents
Equivalent AI agent frameworks
Strong experience with LLMOps, including:
AI model and application evaluation
LLM judges and task-based metrics
MLflow / OpenTelemetry tracing and observability
Prompt and version management
CI/CD for AI applications
AI safety and guardrails
Strong data platform experience with Delta Lake, Apache Iceberg, or ApacheHudi.
Experience with streaming technologies such as Kafka, Kinesis, or GooglePub/Sub.
Hands-on experience with vector databases/search technologies such as DatabricksVector Search, pgvector, Pinecone, Milvus, or Vespa.
Experience with Kubernetes, containers, serverless architectures, andInfrastructure as Code using Terraform and/or CloudFormation.
Strong understanding of microservices, distributed systems, API design, andevent-driven architecture.
Good understanding of web and mobile application architectures.

Qualifications & Experience

• Bachelor's degree in Computer Science, Information Technology, Engineering,Data Science, or a related discipline.
• 12+ years of experience in enterprise application, cloud, dataplatform, or solution architecture.
• Minimum 2 years of experience designing and delivering production-grade GenerativeAI solutions.
• Strong exposure to Data Science and Machine Learning.
• Proven experience leading architecture and technical delivery acrosscomplex enterprise environments.
• Strong client-facing, stakeholder management, communication, and influencingskills.
• Ability to manage multiple technical opportunities and competing priorities.
• Strong analytical and problem-solving skills with the ability to translatecomplex technical concepts into clear business outcomes.
• Required Certifications

Candidates should hold relevant certifications across the following areas:

• AWS, Microsoft Azure, or Google Cloud AI Certifications
• AWS, Microsoft Azure, or Google Cloud Data Science / Machine LearningCertifications
• AWS, Microsoft Azure, or Google Cloud Solution Architect Certifications
• TOGAF 9 Certification
• Other equivalent industry-recognised architecture certifications are anadvantage.