Job Description

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 Security
• JavaScript : React, Next.js, Node.js, TypeScript
• Full-stack development with focus on rapid prototyping

AI/ML Implementation (Applied Engineering Focus)

• LLM Integration : Implementation using OpenAI, Anthropic, Bedrock APIs
• AWS AI Services : Comprehend, Textract, Personalize, Forecast
• RAG Systems : Rapid deployment using managed vector DB solutions
• Model Deployment : Using pre-trained models and managed endpoints
• Service Orchestration : Combining multiple AI services for business solutions
• Experimentation : A/B testing frameworks for AI features

Engineering Approach

• Rapid Delivery : MVP first, iterative improvement approach
• Pragmatic Architecture : Leveraging managed services and existing platforms
• Hypothesis-Driven Development : Build, measure, learn cycles
• Integration Excellence : Connecting best-in-class tools and services
• API-First Design : Microservices, event-driven architectures

Development & Architecture

• Rapid Prototyping : Streamlit, Gradio for quick demonstrations
• API Integration : REST, GraphQL, webhooks, streaming
• Serverless Architecture : Lambda, API Gateway, Step Functions
• Containerization : Docker, Kubernetes for scalable deployments

AWS Platform (Production Focus)

• Bedrock, SageMaker (inference and endpoints)
• Comprehend, Textract, Personalize (managed AI services)
• Lambda, Step Functions for workflow orchestration
• Infrastructure as Code (CDK, Terraform)

Experience Requirements

• 10+ years software engineering with recent hands-on coding
• Track record of rapid delivery - launched multiple AI features in production
• eCommerce AI projects : practical implementations with measurable impact
• Experience balancing speed with quality for optimal business outcomes
• Technical leadership through hands-on contribution

What You'll Work On

• AI solution prototypes with 1-2 week turnaround
• Integration projects connecting LLMs to existing systems
• Production ML features using AWS managed services
• Revenue-generating experiments through A/B testing
• Scalable AI architectures that grow with business needs
• Cross-functional collaboration to identify and solve business problems with AI

Ideal 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 implementations
• Team velocity and technical capability growth
• Quality of architectural decisions balancing speed and scalability
• Adoption rate of AI solutions across the organization

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