### Senior AI Product Engineer - Computer Vision & LLM Systems

# Role Overview
Join Dermalytics as we complete development of our RGB-to-UV skin analysis platform and build AI-powered product recommendations. You'll take over an advanced training system (PyTorch Lightning + AWS SageMaker) with active experiments, complete model deployment, build inference services, and develop skin analysis features powered by computer vision and LLMs.

**This is not a build-from-scratch role** - you'll be completing in-progress work on a solid foundation and expanding into new features.

# About Dermalytics
Dermalytics is building AI-powered skin analysis technology that translates smartphone RGB images into synthetic UV images for professional-grade skin assessment. Our platform helps people understand their skin health without expensive UV imaging devices, democratizing access to dermatological insights.

**We're at an exciting inflection point:**
* Professional ML training infrastructure built and running
* Active experiments on AWS SageMaker with real datasets
* Ready to deploy first production models
* Expanding into comprehensive skin analysis platform

# What You'll Own
This role combines **deep learning engineering (60-70%), full-stack AI development (20-25%), and internal automation (10-15%).**

## 1. RGB→UV Model Completion (Months 1-2)
**Take over active training experiments:**
* Complete training runs for RGB→UV image translation on AWS SageMaker
* Validate model accuracy across diverse skin tones (Fitzpatrick I-VI) and lighting conditions
* Optimize hyperparameters for production quality (learning rate, features, epochs)
* Benchmark performance against MEICE device ground truth (SSIM, PSNR, MAE)
* Document model performance, limitations, and edge cases

**What already exists:**
* ✅ PyTorch Lightning training system (complete implementation)
* ✅ UNet architecture for image-to-image translation
* ✅ Experiments running with real MEICE dataset on S3
* ✅ Training metrics configured (MAE, SSIM, PSNR)
* ✅ AWS SageMaker infrastructure with GPU quotas

**Success metrics:**
* SSIM > 0.85 on validation set
* Model validated across all Fitzpatrick types
* Comprehensive benchmarking report
* Production-ready model weights

## 2. Production Inference Service (Months 3-4)
* Build FastAPI service from infrastructure:
* Develop inference API with model loading from S3/SageMaker
* Implement preprocessing pipeline for mobile RGB images
* Optimize model for mobile deployment (quantization, ONNX export)
* Deploy to AWS SageMaker endpoints with auto-scaling
* Add comprehensive monitoring, logging, and error handling
* Performance testing and optimization (<500ms latency target)

What already exists:
* ✅ AWS CDK stacks for SageMaker endpoints
* ✅ S3 buckets and IAM roles configured
* ✅ Model registry setup
* ❌ Inference service code (needs building)

**Success metrics:**
* API deployed to production with 99.9% uptime
* <500ms inference latency (p95)
* Model size reduced 50% for mobile
* Load testing passed (100+ concurrent requests)

## 3. Skin Analysis Platform (Months 5-7)
* Build comprehensive skin analysis features:
* Develop skin concern detection models (acne, dark spots, wrinkles, texture)
* Implement facial zone mapping for targeted analysis (T-zone, cheeks, forehead)
* Build LLM-powered recommendation engine using OpenAI/Anthropic APIs
* Create product database with ingredient analysis
* Design recommendation logic based on detected concerns
* Integrate all components into cohesive API
* Testing and validation across diverse use cases

**What already exists:**
* ✅ Face detection pipeline (4 methods: OpenCV, dlib, MediaPipe)
* ✅ Image preprocessing utilities
* ❌ Concern detection models (need building)
* ❌ LLM recommendation system (need building)

**Success metrics:**
* 5+ concern types detected with >80% accuracy
* LLM generating relevant, personalized recommendations
* Product database with 100+ SKUs
* End-to-end demo ready for user testing

## 4. Internal AI Automation (20% ongoing)
* Build custom AI bots for business operations:
* Design and implement 3-4 AI automation projects over 12 months
* Create LLM workflows using n8n + OpenAI/Anthropic APIs
* Integrate with business tools (ClickUp, Slack, Shopify, Gmail)
* Examples: Customer service bot, content generation, data analysis
* Maintain and optimize deployed automations

**Success metrics:**
* 3-4 bots deployed and actively used
* Measurable time/cost savings for business functions
* Positive feedback from internal users

## 5. LED Esthetics App Support (10% as needed)
* Maintain existing iOS/Android app:
* Bug fixes and performance improvements
* Feature implementation based on roadmap
* App store deployments and version management
* Coordinate with design team on UI/UX updates

# Technical Stack
## ML & AI:
* PyTorch, PyTorch Lightning
* torchvision, timm (model zoo)
* OpenAI API, Anthropic Claude API
* ONNX, TensorRT (optimization)
* scikit-learn, OpenCV

## Cloud & Infrastructure:
* AWS SageMaker (training & inference)
* AWS S3, Lambda, API Gateway
* AWS CDK (Infrastructure as Code)
* Docker, GitHub Actions

##Backend:
* FastAPI, Pydantic
* SQLAlchemy, Alembic
* PostgreSQL or DynamoDB

## Automation:
* n8n workflow automation
* LangChain (optional)
* OpenAI/Anthropic SDKs

## Development Tools:
* Cursor (AI-assisted coding)
* Claude AI (code review, architecture)
* GitHub Copilot
* Pre-commit hooks (ruff, mypy, bandit)
* pytest

# Qualifications
## Required Skills
**Deep Learning & Computer Vision:**
* 3-5 years professional ML/AI engineering experience
* Strong PyTorch skills with production model deployment
* Understanding of encoder-decoder architectures (UNet, ResNet)
* Experience with image-to-image translation or similar CV tasks
* Knowledge of perceptual metrics (SSIM, PSNR, perceptual loss)
* Ability to evaluate and debug model performance

**Production ML Engineering:**
* Experience with PyTorch Lightning or similar frameworks
* Model optimization techniques (quantization, pruning, ONNX)
* Cloud ML platforms (AWS SageMaker strongly preferred)
* Model serving and inference optimization
* Understanding of distributed training concepts

**Backend Development:**
* Proficiency in FastAPI or Flask for API development
* RESTful API design and best practices
* Python best practices (type hints, testing, documentation)
* Async programming (asyncio, async/await)
* Error handling, logging, and monitoring

**DevOps & Cloud:**
* AWS services experience (SageMaker, S3, Lambda at minimum)
* Docker containerization
* CI/CD pipelines (GitHub Actions or similar)
* Infrastructure as Code familiarity (CDK, Terraform, or CloudFormation)
* Git workflow and collaborative development

**AI Integration:**
* LLM API usage (OpenAI, Anthropic, or similar)
* Prompt engineering and optimization
* Basic understanding of RAG (Retrieval Augmented Generation)
* API integration and error handling

## Strongly Preferred
* PyTorch Lightning hands-on experience
* Image-to-image translation projects in portfolio
* AWS SageMaker training jobs experience
* Model deployment to mobile (CoreML, TensorFlow Lite, ONNX)
* Beauty, healthcare, or dermatology ML applications
* LangChain or similar LLM frameworks

## Nice to Have
Computer vision research publications
GAN experience (Pix2Pix, CycleGAN) - helpful if UNet quality needs improvement
React Native, Swift, or Kotlin for app development
n8n workflow automation
Vector databases (Pinecone, Weaviate, Chroma)
Spanish fluency (for LATAM team collaboration)

# Success Metrics by Timeline
## Month 1-2: Model Training Complete
* RGB→UV model achieves SSIM > 0.85 on validation set
* Model validated across all Fitzpatrick skin types (I-VI)
* Hyperparameter optimization documented
Benchmarking report completed vs. MEICE device
* Production-ready model weights in S3/SageMaker

## Month 3-4: Inference API Deployed
* FastAPI service deployed to AWS SageMaker
* <500ms inference latency (p95) achieved
* Model optimized for mobile (50%+ size reduction)
* 99.9% uptime SLA maintained
* Load testing passed (100+ concurrent requests)
* Monitoring and alerting configured

## Month 5-7: Skin Analysis Platform Live
* 5+ skin concerns detected with >80% accuracy
* Facial zone mapping implemented and tested
* LLM recommendation engine generating relevant suggestions
* Product database with 100+ SKUs and ingredient data
* End-to-end system demo completed
* User testing feedback collected

## Month 6-12: Full Product & Automation
* RGB→UV model continuously improved based on user feedback
* 3-4 internal AI automation bots deployed and used daily
* LED Esthetics app maintained (4.5+ star rating)
* Platform handling production traffic reliably
* Documentation complete for all systems

# Compensation & Benefits
## Compensation:
* Contractor, paid monthly via Deel/Remote.com
* $1,500-2,000 equipment stipend (laptop, monitor)
* AI tools provided (Claude API, Cursor, GitHub Copilot)
* AWS credits for experimentation

## Work Arrangement:
* Fully remote (LATAM, UTC-3 to UTC-6)
* Flexible hours, 4-hour core overlap (10am-2pm EST)
* Focus on results, not hours

## Growth:
* Own complete ML pipeline from training to deployment
* Potential to build/lead AI team as company scales
* Direct impact on product direction

## Why This Role is Compelling:
* **Strong Foundation** - Take over working PyTorch Lightning system with active SageMaker experiments, not starting from scratch
* **Real Impact** - Democratize professional skin analysis (normally $1000+ UV devices) for millions of users
* **Technical Growth** - Work with advanced CV (image-to-image translation), production ML at scale, and modern LLMs
* **True Ownership** - Own complete ML pipeline, make architectural decisions, minimal micromanagement
* **AI-Accelerated** - Leverage Claude/Cursor for rapid development and focus on high-level architecture
* **Work-Life Balance** - Remote-first, flexible hours, no commute, async-friendly culture

# Ideal Candidate Profile
## You're a great fit if you:
* Have built and deployed ML models to production
* Understand both the "research" and "engineering" sides of ML
* Can work independently with minimal direction
* Care about product quality and user experience
* Enjoy solving real-world problems with AI
* Communicate proactively in remote settings
* Balance perfectionism with pragmatism (ship working solutions)
* Use AI tools to accelerate your work
## You're NOT a good fit if you:
* Only want to do pure research (this role ships product)
* Need constant oversight and direction
* Prefer working 9-5 in an office
* Only want to work on one narrow aspect of ML
* Don't enjoy cross-functional work (LLMs, backend, etc.)
* Dislike iterative development and user feedback

Model evaluation

Sagemaker

Fastapi

Flask

Python

AsyncIO

Async await

Error Handling

Logging

Lambda

CI/CD

Terraform

Ai/llm apis

OpenAI

Anthropic

TensorFlow

ONNX

Langchain

React Native

Swift

Kotlin

Pinecone

Weaviate

Senior AI Product Engineer (Computer Vision/PyTorch) - PERM - LATAM