Solution Architect – Gen AI

This is a fully remote position, open to applicants in India.

📋 Description

• Lead collaborative discovery sessions with business and technology stakeholders to gather insights on objectives, limitations, current system frameworks, and integration points.

• Document existing architectural structures and define future state designs, which include system context diagrams, component designs, integration methodologies, and data flows.

• Modernize and design applications to be compatible with cloud environments or to adopt cloud-native architectures utilizing microservices, serverless, and event-driven patterns.

• Develop strategies, roadmaps, and migration frameworks for the transition of applications and data workloads to cloud platforms.

• Engineer AI and ML-enabled solutions, incorporating model integration into products and business processes, ensuring scalable inference and low-latency serving.

• Create design patterns for Generative AI solutions, which encompass retrieval-augmented generation, tool and API integration, prompt and context management, and evaluation methods.

• Establish reference architectures for AI platforms and supporting capabilities, including data pipelines, feature and embedding generation, vector storage, model endpoints, and enterprise API integration.

• Define secure integration frameworks for agent toolchains utilizing MCP, skills and plugin manifests, JSON Schema function calling, OpenAPI tool specifications, and event-driven or message-based execution.

• Integrate security-by-design principles, including identity and access controls, encryption, secrets management, secure networking, and audit logging.

• Collaborate with governance and risk stakeholders on responsible AI, privacy, explainability, safety, compliance, and model risk management.

• Promote solution alignment and adoption by effectively communicating trade-offs, risks, and value, securing stakeholder alignment and governance approvals.

• Assist testing and validation teams, aiding in the triage and resolution of design-related issues during development, UAT, or production phases.

• Perform additional duties as required.

⛳️ Requirements

• Bachelor’s degree in Computer Science, Information Systems, Engineering, Mathematics, Business, or equivalent practical experience.

• Over 5 years of experience in agile software delivery environments with escalating architecture and design responsibilities.

• Proficient in designing distributed systems utilizing microservices and/or serverless patterns.

• Experience in integrating AI and ML capabilities into applications, including considerations for model serving and data dependencies.

• Proficiency in programming languages such as Java, Python, Node.js, or Scala.

• Familiarity with SQL and NoSQL data persistence technologies.

• Experience with AWS, Azure, or Google Cloud and fundamental cloud design patterns.

• Working knowledge of CI/CD pipelines and DevOps methodologies, including automated testing and deployment automation.

• Excellent communication abilities and a knack for translating business requirements into precise technical guidance.

• Practical experience with GenAI and LLM solutions, including retrieval-augmented generation, embeddings, evaluation, and monitoring in production.

• Familiarity with AI and ML platforms or services such as AWS SageMaker, Amazon Bedrock, Azure AI, Azure OpenAI, or Google Vertex AI.

• Knowledge of MCP-based integrations and agent tool standards, including skills and plugin manifests, function calling schemas, and OpenAPI-defined tools.

• Experience with containerization and orchestration tools, such as Docker and Kubernetes, along with cloud container platforms like ECS or EKS.

• Knowledge of vector databases, search technologies, and indexing and retrieval methodologies.

• Experience with enterprise observability, including centralized logging, tracing, metrics, alerting, and operational readiness.

• Background in database and procedural development, including PL/SQL, with strong data modeling principles.

• Understanding of responsible AI practices, governance controls, and AI-specific security considerations.

🏝️ Benefits

• Flexible work environment.

• Fluid career paths.

• Internal mobility is encouraged and celebrated.

• Support for purpose, well-being, and work-life balance.

• A welcoming and inclusive workplace.

• Opportunities to volunteer for meaningful causes that resonate with associates.