senior-ml-engineer

# Senior ML Engineer Production ML engineering patterns for model deployment, MLOps infrastructure, and LLM integration. ## Core Capabilities - **Model deployment** — export to ONNX/TorchScript/SavedModel, containerize, canary rollout, and serve via FastAPI, Triton, TF Serving, TorchServe, or Ray Serve with p95<100ms / error<0.1% gates. - **MLOps pipelines** — feature stores (Feast/Tecton), experiment tracking (MLflow/W&B), model registry, A/B testing, and drift-triggered retraining. - **LLM integration** — provider abstraction, retry/fallback with exponential backoff, token counting, response caching, cost tracking, and Pydantic output validation. - **RAG systems** — vector database selection, chunking strategies, ingestion, retrieval, and reranking. - **Model monitoring** — latency/error tracking, input drift detection (KS test, PSI), prediction-shift alerts, and automated retraining triggers. ## When to Use - Deploying a trained model to production with canary rollout and monitoring. - Standing up MLOps infrastructure (feature store, registry, retraining). - Integrating LLM APIs with provider abstraction and cost control. - Building a RAG pipeline (vector DB + chunking + retrieval + reranking). - Setting up drift detection and model-health alerting. ## Clarify First Before generating artifacts, confirm these inputs. If any is unknown or vague, ASK — do not assume: - [ ] **Task** — model deployment / RAG pipeline build / monitoring setup (selects the script and workflow) - [ ] **Serving target & rollout** — container vs K8s and canary vs direct (drives the generated Dockerfile/manifests and health gates) - [ ] **Model or data interface** — the input/output contract, and for RAG the corpus + vector store (shapes the scaffold) Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact. ## Tools | Tool | Purpose | Command | |------|---------|---------| | `model_deployment_pipeline.py` | Generate deployment artifacts (Dockerfile, K8s manifests, health checks) | `python scripts/model_deployment_pipeline.py --input <path> --output <path> [--config <file>]` | | `rag_system_builder.py` | Scaffold a RAG pipeline with vector store + retrieval logic | `python scripts/rag_system_builder.py --input <path> --output <path> [--config <file>]` | | `ml_monitoring_suite.py` | Set up drift detection, alerting, and dashboards | `python scripts/ml_monitoring_suite.py --input <path> --output <path> [--config <file>]` | All tools support `--verbose`/`-v` and emit JSON (`status`, `start_time`, `end_time`, `processed_items`) to stdout. See [references/tool-reference.md](references/tool-reference.md) for full flag detail. ## References Load the reference that matches the task — keep this file lean and pull detail on demand: - **[references/production-ml-workflows.md](references/production-ml-workflows.md)** — the five step-by-step workflows (model deployment, MLOps setup, LLM integration, RAG, monitoring) with all code templates, serving/vector-DB/chunking/cost tables, the troubleshooting matrix, and success criteria. Read when executing any workflow. - **[references/tool-reference.md](references/tool-reference.md)** — full flag/parameter tables and output formats for the three scripts. Read when scripting the tools. - **[references/mlops_production_patterns.md](references/mlops_production_patterns.md)** — model deployment pipeline with Kubernetes manifests, feature store architecture with Feast examples, model monitoring with drift detection code, A/B testing with traffic splitting, automated retraining with MLflow. Read when building MLOps infra. - **[references/llm_integration_guide.md](references/llm_integration_guide.md)** — provider abstraction layer, retry/fallback with tenacity, prompt templates (few-shot, CoT), token optimization with tiktoken, cost calculation and tracking. Read when integrating an LLM. - **[references/rag_system_architecture.md](references/rag_system_architecture.md)** — RAG pipeline implementation code, vector database comparison/integration, chunking strategies, embedding model selection, hybrid search and reranking. Read when building a RAG system. ## Scope & Limitations **This skill covers:** - End-to-end model deployment pipelines (packaging, containerization, serving, canary rollout) - MLOps infrastructure setup (feature stores, experiment tracking, model registries, retraining) - LLM integration patterns (provider abstraction, retries, caching, cost tracking) - RAG system architecture (vector databases, chunking, retrieval, reranking) **This skill does NOT cover:** - Model training algorithms or hyperparameter tuning (see `senior-data-scientist`) - Raw data pipeline construction and ETL orchestration (see `senior-data-engineer`) - Prompt engineering techniques, few-shot design, or prompt optimization (see `senior-prompt-engineer`) - Image/video model architectures or computer vision inference optimization (see `senior-computer-vision`) ## Integration Points | Skill | Integration | Data Flow | |-------|-------------|-----------| | `senior-data-scientist` | Receives trained models and evaluation metrics for deployment | Data Scientist exports model artifacts and baseline metrics; ML Engineer packages and deploys | | `senior-data-engineer` | Consumes feature pipelines and data quality outputs | Data Engineer builds ETL and feature pipelines; ML Engineer reads from feature store for serving | | `senior-prompt-engineer` | Provides LLM serving infrastructure for prompt workflows | Prompt Engineer designs prompts; ML Engineer deploys provider abstraction and manages cost/latency | | `senior-devops` | Leverages CI/CD and Kubernetes infrastructure for model serving | DevOps manages cluster and pipelines; ML Engineer defines deployment manifests and health checks | | `senior-computer-vision` | Deploys vision models through shared serving infrastructure | CV Engineer trains and exports models; ML Engineer handles Triton/TorchServe deployment and monitoring | | `senior-security` | Applies security scanning to model containers and API endpoints | Security reviews container images and endpoint auth; ML Engineer remediates findings before promotion | --- **Last Updated:** June 2026 **Version:** 1.1.0