Senior AI Engineer

Responsibilities:

• Work with customers, products, and internal teams to understand business problems and turn them into useful AI applications.
• Join customer-facing technical discussions, discovery sessions, demos, and solution design conversations.
• Help shape pilots and proofs of concept, then decide what should be hardened into reusable product functionality.
• Design, build, test, and maintain backend applications and AI workflows using Python and AWS.
• Build and improve systems such as RAG pipelines, agentic workflows with tooling baked in, orchestration workflows, document-processing flows, and agent-backed application services.
• Make thoughtful tradeoffs around performance, cost, reliability, and speed of delivery.
• Contribute to architecture and design decisions with an eye toward systems that are modular, easy to extend, and practical to operate.
• Prototype quickly, evaluate new tools and frameworks, and separate what is genuinely useful from what is just new.
• Help define how we monitor, evaluate, and improve AI systems in production over time.
• Work closely with offshore teams and collaborate effectively across time zones.
• Take part in code reviews, design discussions, and technical planning, and help raise the bar on engineering quality across the team.
• Write clear technical documentation for both internal teams and customer-facing use.

Requirements:

• 5+ years of experience building production software systems.
• Strong Python skills and experience building backend or cloud-native applications.
• Hands-on experience building AI-enabled applications using LLMs, retrieval, automation, or orchestration patterns.
• Experience working with AWS and building cloud-native applications and APIs.
• Good software design instincts, especially around building systems that scale and don't become painful to maintain.
• Experience shipping production-quality software with solid testing, debugging, and code review habits.
• Familiarity with modern data platform concepts such as ingestion pipelines, metadata, governance, access controls, and working with structured, semi-structured, and unstructured data at scale.
• Ability to move between quick prototyping and production work without losing sight of what matters.
• Strong communication skills and comfort working directly with customers to gather feedback, clarify requirements, and turn those into implementation plans.
• Comfort working in a fast-moving environment with a lot of ownership and some ambiguity.
• Experience collaborating with distributed teams across time zones.

Nice to Have:

• Experience with AWS services such as Bedrock, OpenSearch, Lambda, ECS/Fargate, DynamoDB, S3 EventBridge, Athena, Redshift, Glue, and/or Step Functions.
• Experience with RAG systems, document AI, agentic workflows, or intelligent automation use cases.
• Experience working in regulated, enterprise, or public sector environments.
• Experience with modern AWS-native AI tooling and patterns such as Claude on Amazon Bedrock, Amazon Bedrock AgentCore, MCP-based tool integration, guardrails, knowledge bases, and vector search, along with practical experience in agentic workflows, retrieval, and production AI evaluation.
• Experience turning customer-specific builds into reusable internal platforms or product features.