Sr. AI Engineer

Visa: H4EAD,GCEAD,GC,USC,TN Visa, E3 Visa, L2 EAD.

Job Description

Client is investing in enterprise AI capabilities to accelerate delivery, enable self-service technology, and help teams solve meaningful business problems faster. Tulip is a strategic AI platform that provides reusable patterns, tools, and services to make AI-enabled solution delivery easier, safer, and more scalable across the organization.

The Senior AI Engineer - Tulip Platform is a hands-on role responsible for designing, building, and operating Tulip and related AI platform capabilities. The focus is deep technical execution: contributing to architecture and engineering standards, and mentoring peers through design discussion and code review.

The ideal candidate is a pragmatic builder and strong technical partner who can navigate ambiguity, uphold clear technical standards, and consistently deliver high-quality AI solutions aligned to business priorities.

Job Qualifications

• 8+ years of professional software engineering experience with increasing responsibility for architecture, platform delivery, or complex enterprise systems. Hands-on experience designing and delivering production-grade applications, APIs, integrations, and distributed services.
• Strong proficiency in Python and modern software engineering practices such as automated testing, code review, and maintainable design.
• Experience with AI-enabled systems, including large language models, retrieval-augmented generation, agent or workflow orchestration patterns, model integration, evaluation, and responsible AI guardrails.
• Experience with cloud platforms such as Azure, AWS, or GCP, including containerization and infrastructure as code (Docker, Kubernetes, Terraform, Helm), CI/CD, observability, and operational support for production services.
• Experience translating business and product objectives into technical designs and executable engineering work.
• Demonstrated ability to contribute to technical design decisions, mentor less experienced engineers, and work effectively across multiple workstreams.
• Experience securing enterprise services and AI integrations, including OAuth 2.0 and OpenID Connect, secrets management, and automated security scanning in CI/CD.
• Strong communication skills with the ability to explain complex technical decisions to engineering, product, and business stakeholders.
• Bachelor’s degree in Computer Science, Engineering, Information Systems, or related field preferred, or equivalent practical experience.

Preferred Qualifications

• Experience building internal developer platforms, enterprise AI platforms, automation frameworks, or reusable engineering accelerators.
• Experience with Azure AI, Azure OpenAI, Microsoft Fabric, Databricks, vector databases, semantic search, prompt orchestration, or agentic AI frameworks.
• Experience with enterprise AI governance, model and prompt auditability, and responsible AI controls in a compliance-sensitive environment.
• Experience enabling citizen development, self-service workflows, or governed low-code/no-code solution delivery.
• Experience working in retail, restaurant, ecommerce, loyalty, finance, supply chain, or high-volume enterprise environments.
• Experience partnering with product managers, business analysts, forward deployed engineers, and enterprise architects to move ideas from discovery to delivery.

Skills

• AI platform architecture and engineering execution
• LLM, RAG, agentic workflows, vector search, AI evaluation, multi-model integration, and agent interoperability patterns
• API, integration, eventing, and data architecture
• Containers, Kubernetes, and infrastructure as code
• Python, SQL, cloud-native engineering, and secure service design
• Spec-driven development and AI-augmented engineering workflows
• Technical sequencing, dependency awareness, and predictable delivery
• Engineering standards, design reviews, and code quality
• Production readiness, service reliability, SLOs, incident response, and cost awareness
• Peer mentorship, cross-team collaboration, and stakeholder communication

KRA

Platform Architecture and Engineering :

• Contribute to Tulip architecture, patterns, and technical standards so the platform can scale securely and reliably across enterprise use cases.
• Architecture decisions, reusable patterns, design quality, platform stability
• AI Capability Delivery:
• Deliver AI platform capabilities hands-on, including orchestration, RAG, agents, integrations, evaluation, observability, and production support patterns.
• Delivered capabilities, production readiness, adoption by solution teams
• Technical Collaboration and Roadmap Contribution:
• Collaborate with engineers across workstreams, surface dependencies and risks early, and deliver assigned roadmap commitments predictably.
• Roadmap visibility, delivery predictability, blocker resolution

Engineering Excellence:

• Apply and help improve practices for secure, maintainable, testable, observable, and cost-conscious engineering execution.
• Code quality, testing coverage, operational health, defect reduction
• Engineering Practices and Knowledge Sharing:
• Contribute to documentation, design discussions, and knowledge sharing that strengthen how the team builds and operates AI solutions.
• Improved team clarity, stronger handoffs, repeatable delivery practices
• Business Alignment and Value Realization:
• Translate strategic AI objectives into technical outcomes that enable self-service solution delivery and measurable business value.
• Stakeholder alignment, use case enablement, business impact tracking