Computer Systems Analyst

Job Duty Descriptions

• Lead architecture for GenAI/LLM solutions using patterns such as RAG, tool/function calling, and agentic workflows.
• Design secure enterprise knowledge integration and define data access/segregation controls.
• Establish Responsible AI, privacy, and security controls for GenAI.
• Provide technical leadership across stakeholders; document architecture decisions, risks, and roadmaps; guide teams on performance and cost optimization.
• Define AI/ML target architecture and reference patterns aligned to business strategy, enterprise standards, and regulatory requirements.
• Translate business use cases into end-to-end solution designs across data, ML, application, integration, and infrastructure layers.
• Establish technical guardrails (security, privacy, scalability, performance, observability) for AI/ML platforms and workloads.
• Review and approve architecture/designs; document decisions, trade-offs, and standards; mentor teams on best practices.
• Design and govern the AI/ML platform and MLOps lifecycle (data ingestion, training, validation, deployment, monitoring, retraining).
• Define CI/CD standards for ML (model versioning, reproducible pipelines, automated testing, approvals, rollback).

Day-to-day Tasks

• Prototype RAG/agent workflows; tune chunking/retrieval.
• Implement guardrails: input/output filtering, prompt templates, tool whitelisting, rate limits, and secrets management; perform threat modeling and red teaming.
• • Run architecture workshops with engineering teams to capture requirements and constraints.
• Create solution blueprints, component diagrams, and integration flows; define non-functional requirements (NFRs).
• Conduct design reviews, security reviews, and performance reviews; provide actionable remediation guidance.
• Maintain architecture repository and reusable templates.
• Define pipeline templates and guardrails; onboard teams to standard training/deployment workflows.
• Set up model registries, feature stores, and experiment tracking standards; enforce naming/versioning conventions.
• Implement model monitoring.
• Create runbooks and release checklists; support go-lives and post-release validation.
• Perform periodic platform health and cost reviews; recommend tuning and right-sizing actions.

Technology and/or Software used for Duty

• Azure OpenAI / Vertex AI / Bedrock
• LangChain / LlamaIndex
• Vector DB
• C ontent safety/guardrails (Azure AI Content Safety, policy filters)
• Python, APIs, Git/Azure DevOps, Docker/Kubernetes
• Azure/AWS/GCP
• Kubernetes
• MLflow
• Kubeflow / Airflow
• Git / Azure DevOps
• Docker / Kubernetes
• Prometheus / Grafana

Education

Bachelor's degree

Salary Range: $180000 - $230000 a year

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