Senior AI Engineer

Required Qualifications and Experience

•Significant professional experience developing production-grade applications using Python.

•Demonstrated experience designing and delivering enterprise AI, machine learning, data, or cloud-native solutions.

•Strong understanding of Python and/or .NET frameworks and libraries used for APIs, asynchronous processing, data engineering, testing, and AI development.

•Practical experience developing generative AI, retrieval-augmented generation, intelligent search, or AI agent solutions.

•Hands-on experience with AWS-native cloud services and cloud-native architectural patterns.

•Experience designing and integrating REST APIs, event-driven services, databases, enterprise applications, and data platforms.

•Strong understanding of software architecture, distributed systems, microservices, application security, testing, and continuous delivery.

•Experience implementing observability using Datadog or a comparable enterprise observability platform.

•Experience using cloud and application security tools such as Wiz and Snyk.

•Experience with container technologies and orchestration platforms such as Docker, Amazon ECS, or Amazon EKS.

•Experience with automated testing, source control, code review, dependency management, and CI/CD pipelines.

•Strong analytical, troubleshooting, technical writing, and stakeholder communication skills.

•Demonstrated ability to lead technical discussions, mentor engineers, and influence engineering decisions without relying solely on formal authority.

Preferred Qualifications and Experience

•Experience delivering AI solutions in a regulated, financial, public-sector, or risk-sensitive enterprise environment.

•Experience with Amazon Bedrock, vector databases, search platforms, model gateways, and AI evaluation frameworks.

•Familiarity with responsible AI principles, model risk, bias assessment, explainability, human oversight, and AI governance.

•Experience implementing model routing, AI guardrails, prompt security, retrieval security, and agent tool controls.

•Familiarity with infrastructure as code, DevSecOps, MLOps, LLMOps, platform engineering, and site reliability engineering.

•Experience with enterprise identity platforms, private cloud connectivity, API management, and role-based access control.

•Relevant certifications or formal education in software engineering, cloud architecture, cybersecurity, data engineering, artificial intelligence, or a related discipline.

Core Competencies

•Advanced Python and/or .NET engineering.

•Generative AI and retrieval engineering.

•AWS cloud architecture.

•Enterprise system integration.

•Secure software development.

•Technical leadership and mentoring.

•Observability and operational troubleshooting.

•Architecture and design thinking.

•Structured problem-solving.

•Technical risk management.

•Stakeholder communication.

•Team collaboration.

•Continuous learning and improvement.