Data Software Engineer-AI

Job description

7 -10 years • More on DevOps, and AI Ops (used when Agentic AI comes into picture) Platform & Framework Development • Build reusable agentic AI frameworks, orchestration templates, and accelerators • Develop shared libraries for: • Tool orchestration • Agent communication • Memory handling • Workflow management • Evaluation pipelines • Create standardized development patterns for enterprise AI systems MCP & Integration Enablement • Develop and maintain MCP-compatible integrations and enterprise connectors • Build reusable APIs and integration services for enterprise platforms • Enable scalable access to enterprise data sources and business tools Governance & TRiSM Enablement • Define lightweight governance standards for agentic AI systems • Implement traceability, monitoring, logging, and lifecycle tracking mechanisms • Support trust, risk, security, and monitoring (TRiSM) compliance • Establish evaluation, observability, and auditability practices for AI agents Engineering Enablement • Create reusable templates, starter kits, and deployment accelerators • Improve developer productivity through standardized tooling and automation • Establish documentation standards and reusable implementation guides Operational Excellence • Support production readiness for AI systems • Define monitoring, telemetry, guardrails, and fallback strategies • Collaborate with solution teams to improve reliability and maintainability Required Skills Technical Skills • Strong software architecture and platform engineering experience • Expertise in: • LangGraph / Semantic Kernel / AutoGen / CrewAI • Orchestration frameworks • AI system architecture • MCP ecosystem concepts • Strong backend engineering experience using Python/Node.js • Experience building reusable platforms/frameworks • Strong understanding of: • AI governance • Security • Observability • Evaluation frameworks • Monitoring systems • Experience with APIs, event-driven architectures, and cloud-native systems Good to have Skills • Experience with enterprise governance frameworks • Familiarity with AI risk management and compliance practices • Experience with DevSecOps and platform engineering • Knowledge of vector databases, RAG systems, and enterprise search architectures