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Tech Stack
Tools & technologiesAWSAzureCloudGoogle Cloud PlatformJavaMicroservicesPythonTypeScript
About the role
Key responsibilities & impact- Design and implement Automation and Agentic AI systems to support data governance, enablement and stewardship activities.
- Responsible for designing the solution using approved architecture patterns, developing orchestration logic, tool use, and memory strategies.
- Developing last mile automation and AI capabilities to enable around data access management and automation.
- Develop production‑quality code for basic automation, AI agents, services, and supporting infrastructure.
- Build agents capable of executing multi‑step workflows, interacting with enterprise data, metadata, and knowledge systems, reasoning over policies, standards, and governance rules.
- Integrate LLM‑based agents with existing data platforms, governance tools, catalogs, document repositories, and APIs.
- Apply modern software engineering best practices including modular design, version control, testing automation, observability, and CI/CD pipelines.
- Package and deploy AI solutions into development, test, and production environments.
- Monitor agent behavior, performance, and outputs to ensure reliability, traceability, and policy compliance.
- Diagnose and remediate failures, hallucinations, workflow breaks, or data quality dependencies.
- Refactor prototypes into scalable, maintainable production services.
- Support the expansion of successful agents from team‑level solutions to company‑wide platforms.
- Engineer guardrails to enforce data governance, privacy, security, and Responsible AI principles.
- Implement logging, auditing, explainability, and versioning for AI agents and prompts.
- Collaborate with governance and legal partners to operationalize Responsible AI controls in code and architecture.
- Develop agents that improve knowledge capture, classification, retrieval, and reuse.
- Produce technical documentation, architecture diagrams, and runbooks for AI solutions.
Requirements
What you’ll need- Bachelor’s degree in computer science, Software Engineering, Data Science, or equivalent practical experience.
- Strong hands‑on software engineering experience, with demonstrated delivery of production systems.
- Experience developing LLM‑powered or AI‑driven applications, including orchestration, prompt engineering, and tool integration.
- Proficiency in one or more modern programming languages (e.g., Python, TypeScript, Java).
- Experience working with APIs, microservices, and cloud‑based architectures.
- Familiarity with data management, metadata, data quality, governance, or knowledge systems.
- Ability to move from ambiguous problem statements to implemented, running systems.
- Preferred Qualifications Direct experience building Agentic AI architectures using frameworks such as LangChain, Semantic Kernel, AutoGen, or similar.
- Experience with enterprise data catalogs, governance platforms, or knowledge management systems.
- Cloud deployment experience (e.g., Azure, AWS, or GCP), including security and identity integration.
- Experience operationalizing AI: monitoring, cost control, reliability, and model lifecycle management.
- Background in Responsible AI, compliance engineering, or AI risk mitigation.
Benefits
Comp & perks- Health insurance
- Flexible work arrangements
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
automationAI systemsorchestration logicproduction-quality codeLLM-based agentsmodular designversion controlCI/CD pipelinesprompt engineeringcloud deployment
Soft Skills
problem-solvingcollaborationcommunicationdiagnosing failuresremediationdocumentation
Certifications
Bachelor’s degree in computer scienceBachelor’s degree in Software EngineeringBachelor’s degree in Data Science