Agentic AI Engineer

CES improvements: PR-level scoring, historical context, context-retrieval agent, automated score correction, training data prep from feedback.

Data/PR tracking: ingest PRs, link to commits/branches, track merges to main, backfills.

Orchestration & reliability: Dagster migration, retries, scheduling, monitoring, data quality alerts.

LLM/prompt optimization: DSPy-based prompt tuning, eval set creation, feedback-driven corrections, prompt cost/quality tradeoffs.

Metadata & classification: role/work-type classification, epic linking improvements, filters/explorer features.

Agentic coding: build retrieval agents that pull code context/diffs/history to improve scoring (aligns with CES Phase 3 and quality metric work).

Data pipelines: Dagster (preferred) or equivalent orchestrator; AWS-native pipelines (ECS/EventBridge/Lambda/S3/Glue experience a plus); robust backfills and retries.

LLM/Prompting: DSPy or similar prompt/policy optimization; prompt chaining; context packing; eval design and execution.

Data modeling & analytics: PR/commit linking, branch/main tracking, epic linking; quality/effort scoring signals; anomaly detection for data quality.

Backend integration: building services that ingest PR/commit metadata, compute CES/quality metrics, and expose them to UI/API.

Eval/feedback loops: design and run eval sets; collect user feedback; close the loop with automated/manual score correction.

Experience with code-scoring/effort or quality metrics; SonarQube integration.

Graph-ish linking across artifacts (Jira/PR/commit/branch) for epic/work-type classification.

Experience tuning performance/cost for LLM calls (OSS LLM evals, batching, caching).