Cloud Engineer- Data/AI Focused at Innovative Solutions

Job description

As a Data & AI Engineer, you’ll build and deploy modern data pipelines and generative AI solutions on AWS — from scalable ETL/ELT workflows and cloud data platforms to production-grade RAG systems and AI agents. One engagement you may be designing and optimizing data pipelines using Glue, Lambda, and Redshift, the next you’re building GenAI applications powered by Bedrock, Claude, and vector databases to enable intelligent search and automation. You’ll work directly with clients to deliver end-to-end data and AI solutions that are secure, scalable, and production-ready. What You’ll Do: 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 Required Skills: 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 Preferred: 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

Requirements

Must-haves

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

Nice-to-haves

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

Application prep

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Keywords to mirror

aiawssqlmlagilescrum

Platform: lever
Effort: quick

Benefits

Benefits detected in the listing.

Vision