4 days ago
Remote, India or Bengaluru, IndiaSenior
Responsibilities
- Lead discovery with business and technology partners and document current-state systems, constraints, integrations, and objectives.
- Define target-state architectures, system context diagrams, component designs, integration patterns, data flows, strategies, roadmaps, and migration plans.
- Design cloud-compatible and cloud-native applications using microservices, serverless, and event-driven patterns.
- Design AI, ML, and Generative AI solutions including model integration, inference, RAG, tool and API integration, prompt and context management, and evaluation approaches.
- Define AI platform reference architectures covering data pipelines, feature and embedding generation, vector storage, model endpoints, and enterprise API integrations.
- Design secure agent toolchain integrations using MCP, plugin manifests, JSON Schema function calling, OpenAPI tool specifications, and event-driven or message-based execution.
- Incorporate identity, access controls, encryption, secrets management, secure networking, audit logging, privacy, explainability, safety, compliance, and model risk controls.
- Communicate tradeoffs, risks, and value; obtain stakeholder alignment and governance approvals; and support testing, validation, UAT, production issue triage, and operational readiness.
Requirements
- Bachelor’s degree in Computer Science, Information Systems, Engineering, Mathematics, Business, or equivalent practical experience.
- 5+ years of experience in agile software delivery environments with increasing architecture and design responsibility.
- Experience designing distributed systems with microservices and/or serverless patterns.
- Experience integrating AI and ML capabilities into applications, including model serving and data dependencies.
- Experience with one or more of Java, Python, Node.js, or Scala.
- Experience with SQL and NoSQL data persistence technologies.
- Experience with AWS, Azure, or Google Cloud and core cloud design patterns.
- Working knowledge of CI/CD pipelines, DevOps practices, automated testing, and deployment automation.
- Strong communication skills and ability to translate business needs into technical direction.
- Preferred experience with Generative AI and LLM solutions, RAG, embeddings, evaluation, production monitoring, AI platforms, MCP integrations, secure enterprise integrations, Docker, Kubernetes, vector databases, search technologies, observability, PL/SQL, data modeling, responsible AI, governance, and AI security.
Benefits
- Flexible work environment, fluid career paths, and opportunities for internal mobility.
- Focus on purpose, well-being, work-life balance, inclusion, and community volunteering.
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