Overview
Skills
Job Details
As an AWS Bedrock AI Engineer, you will play a pivotal role in designing, building, and optimizing generative AI solutions using AWS Bedrock and other AWS AI/ML services. You will collaborate with cross-functional teams, including data scientists, ML engineers, and software developers, to integrate LLMs (Large Language Models) and foundation models into enterprise-grade applications, ensuring scalability, performance, and usability.
Design & Build AI Solutions: Leverage AWS Bedrock and other AWS AI/ML services to design, optimize, and deploy state-of-the-art generative AI solutions tailored to enterprise needs.
Collaborate & Integrate: Work alongside data scientists, ML engineers, and software developers to integrate large-scale AI models (such as LLMs and foundation models) into production systems, ensuring alignment with business objectives and user requirements.
API & Pipeline Development: Build scalable APIs and pipelines for seamless AI model deployment, monitoring, and lifecycle management, with a focus on automation and CI/CD integration.
Security, Compliance & Optimization: Ensure all solutions comply with security, governance, and compliance standards, while also focusing on cost optimization in cloud-native environments.
Research Emerging Models: Continuously evaluate emerging foundation models such as Anthropic Claude, Cohere, AI21, Stability AI, etc., and provide recommendations on the most suitable models to integrate with AWS Bedrock.
Business Collaboration: Partner with business stakeholders to understand and translate requirements into comprehensive AI solutions that prioritize performance, scalability, and usability.
< data-start="2270" data-end="2306">Your Skills and Experience:</>
Cloud AI/ML Ecosystem Experience: Strong hands-on experience working within a major cloud AI/ML ecosystem (AWS, Azure, or Google Cloud Platform). Specific expertise in AWS Bedrock, SageMaker, Lambda, and Step Functions is highly desirable.
Programming Expertise: Proficiency in Python for model integration, API development, and automating AI workflows.
LLMs & Foundation Models: Practical experience working with LLMs, including working with models like GPT, BERT, or T5, and familiarity with Retrieval-Augmented Generation (RAG) techniques and embeddings.
Vector Databases & Frameworks: Familiarity with vector databases (e.g., Pinecone, Weaviate, FAISS) and frameworks like LangChain, for integrating AI models into applications.
Production Experience: Demonstrated experience in deploying AI/ML models into production or piloting generative AI applications in cloud or hybrid environments.
MLOps & CI/CD: A strong understanding of MLOps practices, including model versioning, monitoring, and continuous integration/continuous delivery (CI/CD) for machine learning.
Security & Governance: Awareness of security, compliance, and governance considerations when building and deploying AI systems in the cloud.
< data-start="3654" data-end="3687">Set Yourself Apart With:</>
AWS Bedrock Experience: Prior experience delivering enterprise-level generative AI projects using AWS Bedrock and integrating with other AWS AI/ML services.
Multi-Cloud Expertise: Familiarity with multi-cloud AI/ML services (such as Azure OpenAI, Google Cloud Platform Vertex AI, Hugging Face Hub) and the ability to recommend and implement cross-cloud solutions.
Open-Source Contributions: Contributions to open-source AI frameworks or published work related to generative AI will be a significant advantage.
Cost Optimization & Performance Tuning: Expertise in cost optimization and performance tuning for large-scale AI applications, particularly in AWS.
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