Innovative Solutions - Cloud Engineer- Data/AI Focused
Requirements
• 5+ years of software engineering experience with at least 2+ years focused on AI/ML, data engineering, or cloud-native development • 2+ years of hands-on AWS experience with production deployments • 1+ years of direct Generative AI experience (LLMs, embeddings, RAG, agents) • Proven track record delivering production AI applications from concept to deployment • Strong understanding of software engineering best practices (version control, testing, code review, documentation) • Experience working in agile/scrum environments with distributed teams • Excellent problem-solving skills and ability to work independently with minimal supervision • Strong written and verbal communication skills for client-facing interactions • Technical familiarity with AWS data and AI/ML services and modern data engineering practices • Hands-on experience with ETL/ELT processes and data transformation • Exposure to Generative AI concepts including LLMs, embeddings, RAG, and agent frameworks • Ability to write and optimize SQL queries across various database platforms • Knowledge of data modeling concepts and best practices • Strong analytical and problem-solving skills • Eagerness to learn new technologies and keep up with cloud and AI innovations • Excellent communication skills with the ability to explain technical concepts clearly • Attention to detail and commitment to solution quality • Collaborative mindset with strong teamwork capabilities • Experience or interest in automation and infrastructure as code • $100,000 - $160,000 a year
Responsibilities
• Implementing data pipelines using AWS services such as Glue, Lambda, Step Functions, and EMR • Creating and maintaining data extraction, transformation, and loading processes • Configuring and optimizing AWS database services including RDS, Aurora, Redshift, and DynamoDB • Implementing data lakes using S3 and related AWS services • Designing and building production-ready Generative AI applications using Amazon Bedrock and foundation models such as Anthropic Claude • Building and optimizing RAG (Retrieval-Augmented Generation) pipelines with vector databases • Developing AI agents and multi-agent orchestration systems using frameworks like LangChain or LlamaIndex • Writing and testing SQL queries and stored procedures • Documenting technical solutions and providing knowledge transfer to customers • Supporting the implementation of data governance and security controls • Troubleshooting and resolving issues with data pipelines and AI services • Participating in code reviews and implementing feedback • Assisting with proof-of-concept implementations for customer engagements • We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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