# Skills Senior Ml Engineer

> Senior ML Engineer

- Skill: `urjuyaimon09/skills-senior-ml-engineer` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add urjuyaimon09/skills-senior-ml-engineer`
- Raw SKILL.md: https://api.skillmd.com/api/skills/urjuyaimon09/skills-senior-ml-engineer/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: urjuyaimon09 (https://skillmd.com/u/urjuyaimon09)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/urjuyaimon09/skills-senior-ml-engineer

---

Senior ML Engineer

Production ML engineering patterns for model deployment, MLOps infrastructure, and LLM integration.

Table of Contents
Model Deployment Workflow
MLOps Pipeline Setup
LLM Integration Workflow
RAG System Implementation
Model Monitoring
Reference Documentation
Tools
Model Deployment Workflow

Deploy a trained model to production with monitoring:

Export model to standardized format (ONNX, TorchScript, SavedModel)
Package model with dependencies in Docker container
Deploy to staging environment
Run integration tests against staging
Deploy canary (5% traffic) to production
Monitor latency and error rates for 1 hour
Promote to full production if metrics pass
Validation: p95 latency < 100ms, error rate < 0.1%
Container Template
FROM python:3.11-slim

COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY model/ /app/model/
COPY src/ /app/src/

HEALTHCHECK CMD curl -f http://localhost:8080/health || exit 1

EXPOSE 8080
CMD ["uvicorn", "src.server:app", "--host", "0.0.0.0", "--port", "8080"]

Serving Options
Option	Latency	Throughput	Use Case
FastAPI + Uvicorn	Low	Medium	REST APIs, small models
Triton Inference Server	Very Low	Very High	GPU inference, batching
TensorFlow Serving	Low	High	TensorFlow models
TorchServe	Low	High	PyTorch models
Ray Serve	Medium	High	Complex pipelines, multi-model
MLOps Pipeline Setup

Establish automated training and deployment:

Configure feature store (Feast, Tecton) for training data
Set up experiment tracking (MLflow, Weights & Biases)
Create training pipeline with hyperparameter logging
Register model in model registry with version metadata
Configure staging deployment triggered by registry events
Set up A/B testing infrastructure for model comparison
Enable drift monitoring with alerting
Validation: New models automatically evaluated against baseline
Feature Store Pattern
from feast import Entity, Feature, FeatureView, FileSource

user = Entity(name="user_id", value_type=ValueType.INT64)

user_features = FeatureView(
    name="user_features",
    entities=["user_id"],
    ttl=timedelta(days=1),
    features=[
        Feature(name="purchase_count_30d", dtype=ValueType.INT64),
        Feature(name="avg_order_value", dtype=ValueType.FLOAT),
    ],
    online=True,
    source=FileSource(path="data/user_features.parquet"),
)

Retraining Triggers
Trigger	Detection	Action
Scheduled	Cron (weekly/monthly)	Full retrain
Performance drop	Accuracy < threshold	Immediate retrain
Data drift	PSI > 0.2	Evaluate, then retrain
New data volume	X new samples	Incremental update
LLM Integration Workflow

Integrate LLM APIs into production applications:

Create provider abstraction layer for vendor flexibility
Implement retry logic with exponential backoff
Configure fallback to secondary provider
Set up token counting and context truncation
Add response caching for repeated queries
Implement cost tracking per request
Add structured output validation with Pydantic
Validation: Response parses correctly, cost within budget
Provider Abstraction
from abc import ABC, abstractmethod
from tenacity import retry, stop_after_attempt, wait_exponential

class LLMProvider(ABC):
    @abstractmethod
    def complete(self, prompt: str, **kwargs) -> str:
        pass

@retry(stop=stop_after_attempt(3), wait=wait_exponential(min=1, max=10))
def call_llm_with_retry(provider: LLMProvider, prompt: str) -> str:
    return provider.complete(prompt)

Cost Management
Provider	Input Cost	Output Cost
GPT-4	$0.03/1K	$0.06/1K
GPT-3.5	$0.0005/1K	$0.0015/1K
Claude 3 Opus	$0.015/1K	$0.075/1K
Claude 3 Haiku	$0.00025/1K	$0.00125/1K
RAG System Implementation

Build retrieval-augmented generation pipeline:

Choose vector database (Pinecone, Qdrant, Weaviate)
Select embedding model based on quality/cost tradeoff
Implement document chunking strategy
Create ingestion pipeline with metadata extraction
Build retrieval with query embedding
Add reranking for relevance improvement
Format context and send to LLM
Validation: Response references retrieved context, no hallucinations
Vector Database Selection
Database	Hosting	Scale	Latency	Best For
Pinecone	Managed	High	Low	Production, managed
Qdrant	Both	High	Very Low	Performance-critical
Weaviate	Both	High	Low	Hybrid search
Chroma	Self-hosted	Medium	Low	Prototyping
pgvector	Self-hosted	Medium	Medium	Existing Postgres
Chunking Strategies
Strategy	Chunk Size	Overlap	Best For
Fixed	500-1000 tokens	50-100	General text
Sentence	3-5 sentences	1 sentence	Structured text
Semantic	Variable	Based on meaning	Research papers
Recursive	Hierarchical	Parent-child	Long documents
Model Monitoring

Monitor production models for drift and degradation:

Set up latency tracking (p50, p95, p99)
Configure error rate alerting
Implement input data drift detection
Track prediction distribution shifts
Log ground truth when available
Compare model versions with A/B metrics
Set up automated retraining triggers
Validation: Alerts fire before user-visible degradation
Drift Detection
from scipy.stats import ks_2samp

def detect_drift(reference, current, threshold=0.05):
    statistic, p_value = ks_2samp(reference, current)
    return {
        "drift_detected": p_value < threshold,
        "ks_statistic": statistic,
        "p_value": p_value
    }

Alert Thresholds
Metric	Warning	Critical
p95 latency	> 100ms	> 200ms
Error rate	> 0.1%	> 1%
PSI (drift)	> 0.1	> 0.2
Accuracy drop	> 2%	> 5%
Reference Documentation
MLOps Production Patterns

references/mlops_production_patterns.md contains:

Model deployment pipeline with Kubernetes manifests
Feature store architecture with Feast examples
Model monitoring with drift detection code
A/B testing infrastructure with traffic splitting
Automated retraining pipeline with MLflow
LLM Integration Guide

references/llm_integration_guide.md contains:

Provider abstraction layer pattern
Retry and fallback strategies with tenacity
Prompt engineering templates (few-shot, CoT)
Token optimization with tiktoken
Cost calculation and tracking
RAG System Architecture

references/rag_system_architecture.md contains:

RAG pipeline implementation with code
Vector database comparison and integration
Chunking strategies (fixed, semantic, recursive)
Embedding model selection guide
Hybrid search and reranking patterns
Tools
Model Deployment Pipeline
python scripts/model_deployment_pipeline.py --model model.pkl --target staging


Generates deployment artifacts: Dockerfile, Kubernetes manifests, health checks.

RAG System Builder
python scripts/rag_system_builder.py --config rag_config.yaml --analyze


Scaffolds RAG pipeline with vector store integration and retrieval logic.

ML Monitoring Suite
python scripts/ml_monitoring_suite.py --config monitoring.yaml --deploy


Sets up drift detection, alerting, and performance dashboards.

Tech Stack
Category	Tools
ML Frameworks	PyTorch, TensorFlow, Scikit-learn, XGBoost
LLM Frameworks	LangChain, LlamaIndex, DSPy
MLOps	MLflow, Weights & Biases, Kubeflow
Data	Spark, Airflow, dbt, Kafka
Deployment	Docker, Kubernetes, Triton
Databases	PostgreSQL, BigQuery, Pinecone, Redis