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GenAI Workflow & Service Development
GenAI Workflow & Service Development
This role involves designing, implementing, and maintaining Python-based services and workflows that integrate LLMs and GenAI capabilities. The position focuses on building agentic and multi-step workflows using orchestration frameworks and integrating GenAI into existing systems while ensuring operational health and continuous improvement.
About the role
GenAI Workflow & Service Development
- Design, implement, and maintain Python-based services and workflows that integrate LLMs and GenAI capabilities with internal platforms and applications.
- Build and iterate on agentic and multi-step workflows using approved orchestration frameworks and platform patterns (e.g., LangGraph, AgentCore, LangChain).
- Consume existing retrieval/RAG and search abstractions to improve response quality, grounding, and reliability, tuning parameters (top-k, scoring, filters) rather than re-implementing core retrieval infrastructure.
- Develop robust tooling and APIs for agents, including clear input/output schemas, error contracts, versioning, and observability hooks.
- Implement structured output handling (e.g., JSON schemas, tool calls) to ensure predictable behavior and easier downstream integration.
Platform Integration & Governance
- Operate within established platform, security, and governance guardrails (RBAC, data access boundaries, PII handling, logging, audit) instead of building custom, one-off mechanisms.
- Leverage existing platform SDKs, templates, and patterns for configuration, deployment, and monitoring of GenAI workloads.
- Work in a cloud-native AWS and Azure environments (e.g., cloud native applications, environment variables, secrets management, logging/metrics/tracing), collaborating with platform teams as needed rather than owning core infra design.
Collaboration & Delivery
- Partner with product managers, internal business stakeholders, and UX to translate problem statements and evaluation criteria into concrete, production-ready workflows.
- Collaborate with data and application teams to integrate GenAI capabilities into existing systems (e.g., internal tools, portals, automation flows) with minimal disruption.
- Participate in design reviews, code reviews, and architecture discussions, ensuring solutions are maintainable, observable, and aligned with platform standards.
- Contribute to internal enablement (playbooks, examples, patterns) to help eTech Engineering and other teams become effective consumers of GenAI capabilities.
Operations & Continuous Improvement
- Own the operational health of GenAI workflows you build: monitoring, alerting, troubleshooting, and iterative improvement.
- Incorporate evaluation and guardrail checks (e.g., automated tests, evaluation harnesses, red/blue team feedback) into workflows to improve quality and safety over time.
- Act as a “high adopter of AI to build AI”, continuously using AI tools (e.g., Glean, Devin, Windsurf, Claude) to accelerate design, development, testing, and documentation.