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.

Experience
4-8 yrs