AI ML ENGINEER

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., 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

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 Google Cloud Platform, 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., 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