AWS AI Engineer

AI Solution Development
•    Design, develop, and deploy enterprise AI applications using AWS services and modern AI frameworks.
•    Build Generative AI, Conversational AI, AI Assistant, and Agentic AI solutions.
•    Translate business requirements into scalable, resilient, and secure AI applications.
•    Develop reusable AI frameworks, APIs, integration services, and accelerators.
•    Collaborate with architects and business stakeholders to deliver AI driven business outcomes.

Generative AI & Agentic AI Development
•    Build applications leveraging foundation models and Large Language Models (LLMs).
•    Design and implement Retrieval Augmented Generation (RAG) architectures using enterprise knowledge sources.
•    Develop AI agents and multi agent orchestration workflows.
•    Implement prompt engineering, context management, memory patterns, and evaluation frameworks.
•    Evaluate and integrate commercial and open source models based on performance, cost, scalability, and security requirements.
•    Develop AI powered assistants capable of automating business processes and decisionmaking workflows.

AWS AI & Cloud Development
•    Design and develop AI solutions leveraging Amazon Bedrock, Amazon SageMaker, Amazon Q, OpenSearch, Lambda, ECS, EKS, and related AWS services.
•    Develop cloud native AI applications utilizing serverless and container based architectures.
•    Build scalable APIs and microservices supporting AI workloads.
•    Integrate AI solutions with enterprise applications, business systems, and data platforms.
•    Ensure high availability, security, reliability, observability, and operational excellence.

Intelligent Automation & Business Process Integration
•    Design AI powered workflow automation solutions integrating AWS AI capabilities with enterprise systems.
•    Build intelligent process automation solutions leveraging AI services, APIs, and workflow orchestration tools.
•    Integrate AI capabilities into business applications to improve operational efficiency and employee productivity.
•    Develop reusable automation frameworks and enterprise integration patterns.

Open Source AI & Model Engineering
•    Deploy and optimize open source foundation models including Llama, Mistral, and similar models.
•    Fine tune foundation models for business specific use cases.
•    Build and manage model serving and inference environments.
•    Support model lifecycle management, governance, monitoring, testing, and version control.

Model Optimization & Performance Engineering
•    Optimize AI solutions for latency, throughput, scalability, and operational efficiency.
•    Apply quantization, model compression, distillation, pruning, and inference optimization techniques.
•    Optimize GPU utilization and infrastructure performance.
•    Design cost efficient AI architectures balancing performance, business value, and cloud spend.
•    Monitor AI applications and continuously improve model effectiveness and reliability.

DevOps, MLOps & AI Operations
•    Implement CI/CD pipelines supporting AI application delivery.
•    Build MLOps and LLMOps processes for model deployment, testing, monitoring, and governance.
•    Support production operations and troubleshooting of enterprise AI solutions.
•    Ensure compliance with security, Responsible AI, and enterprise governance standards.

Collaboration & Technical Leadership
•    Collaborate with AI Architects, Data Scientists, Product Owners, Developers, and Business Stakeholders.
•    Participate in solution design, architecture reviews, and code reviews.
•    Mentor junior engineers and promote AI engineering best practices.
•    Stay current with emerging AI technologies, tools, frameworks, and industry trends.
•    Support client demonstrations, workshops, technical proposals, and innovation initiatives.