Lead AI / ML Engineer - Associate Director / Director Level
Position Overview We are seeking a hands-on Lead AI / ML Engineer with strong software engineering expertise to rapidly build and deploy AI solutions. This role focuses on practical implementation and quick delivery of business value through intelligent integration of AWS ML services, LLMs, and modern AI tools. This hybrid position requires 75% hands-on development and 25% technical leadership, emphasizing speed to market and pragmatic engineering decisions.
Key Responsibilities
- Rapidly prototype and deploy AI solutions using pre-built AWS ML services and LLM APIs
- Build full-stack applications that integrate existing ML / LLM tools and services
- Focus on "time to value" - quick iterations, hypothesis testing, A / B experiments
- Orchestrate multiple AI services (Bedrock, SageMaker endpoints, third-party APIs)
- Develop proof-of-concepts in days / weeks using available tools and platforms
- Lead technical team through hands-on coding and architecture decisions
- Champion pragmatic "buy and integrate" approaches for faster delivery
Required Technical Skills
Programming Languages & Frameworks (Expert Level Required)
Python : FastAPI, Django, Flask (expert proficiency)Java : Spring Boot, Spring Cloud, Spring SecurityJavaScript : React, Next.js, Node.js, TypeScriptFull-stack development with focus on rapid prototypingAI / ML Implementation (Applied Engineering Focus)
LLM Integration : Implementation using OpenAI, Anthropic, Bedrock APIsAWS AI Services : Comprehend, Textract, Personalize, ForecastRAG Systems : Rapid deployment using managed vector DB solutionsModel Deployment : Using pre-trained models and managed endpointsService Orchestration : Combining multiple AI services for business solutionsExperimentation : A / B testing frameworks for AI featuresEngineering Approach
Rapid Delivery : MVP first, iterative improvement approachPragmatic Architecture : Leveraging managed services and existing platformsHypothesis-Driven Development : Build, measure, learn cyclesIntegration Excellence : Connecting best-in-class tools and servicesAPI-First Design : Microservices, event-driven architecturesDevelopment & Architecture
Rapid Prototyping : Streamlit, Gradio for quick demonstrationsAPI Integration : REST, GraphQL, webhooks, streamingServerless Architecture : Lambda, API Gateway, Step FunctionsContainerization : Docker, Kubernetes for scalable deploymentsAWS Platform (Production Focus)
Bedrock, SageMaker (inference and endpoints)Comprehend, Textract, Personalize (managed AI services)Lambda, Step Functions for workflow orchestrationInfrastructure as Code (CDK, Terraform)Experience Requirements
10+ years software engineering with recent hands-on codingTrack record of rapid delivery - launched multiple AI features in productioneCommerce AI projects : practical implementations with measurable impactExperience balancing speed with quality for optimal business outcomesTechnical leadership through hands-on contributionWhat You'll Work On
AI solution prototypes with 1-2 week turnaroundIntegration projects connecting LLMs to existing systemsProduction ML features using AWS managed servicesRevenue-generating experiments through A / B testingScalable AI architectures that grow with business needsCross-functional collaboration to identify and solve business problems with AIIdeal Candidate Profile A pragmatic engineer who excels at quickly translating business requirements into working AI solutions. Someone who understands that the best solution is often the one that ships fastest and delivers immediate value. You thrive in environments where rapid experimentation and iteration are valued over lengthy development cycles. Your strength lies in knowing when to use existing tools versus building custom solutions, always optimizing for business impact and speed to market.
Success Metrics
Speed of AI feature deployment (idea to production)Business value delivered through AI implementationsTeam velocity and technical capability growthQuality of architectural decisions balancing speed and scalabilityAdoption rate of AI solutions across the organization