AI Engineer

Required Skills & Experience

• 5+ years of software/development engineering experience
• Strong Python development experience with clean coding, testing, and software engineering best practices
• Experience working with cloud environments (AWS preferred, Azure/GCP acceptable)
• Hands-on experience integrating LLMs, AI services, or AI-assisted applications into business workflows
• Strong understanding of data management, data pipelines, and enterprise data architecture
• Experience working with technologies such as Snowflake, databases, data lakes, or modern data stacks Experience building and supporting production systems with monitoring, testing, CI/CD, and operational ownership

Nice to Have Skills & Experience

• AWS Bedrock experience
• Knowledge of RAG (Retrieval-Augmented Generation) architectures
• Experience with knowledge graphs, GraphRAG, or knowledge representation techniques
• Experience building AI assistants, copilots, or workflow automation solutions
• Familiarity with agentic AI frameworks and multi-agent systems
• AI observability, evaluation, and monitoring experience
• Experience integrating enterprise systems and APIs
• Experience in regulated industries such as financial services, healthcare, or insurance Experience mentoring junior developers or contributing to engineering best practices

Job Description

strong software engineer with expertise in Python, cloud technologies, and production AI applications. You'll join a small, highly autonomous team building AI-powered solutions that solve real business problems using enterprise data.

The ideal candidate has hands-on experience developing and deploying AI applications, assistants, or workflow automation tools, along with a strong understanding of data platforms such as Snowflake and modern cloud environments. This is not a research-focused AI role; instead, we're looking for someone who can apply AI effectively, build scalable solutions, and own products from development through production support while helping shape the organization's AI strategy

• Design, develop, and deploy AI-powered applications and assistants
• Write production-quality Python code and own features from design through deployment
• Build and maintain cloud-based AI solutions within AWS and evolving AI ecosystems
• Create and manage data pipelines that leverage structured and unstructured enterprise data
• Develop knowledge management and retrieval solutions to improve AI accuracy and usefulness
• Integrate AI capabilities with enterprise platforms, APIs, and internal services
• Evaluate AI model performance and implement improvements based on measurable outcomes Monitor system quality, reliability, cost, and performance in production environments