Senior AI Engineer

Technology Strategy & Vision

- Define multi-year architectural direction across full-stack systems cloud platforms and AI-driven products focusing on scale resilience security and global performance.

-Establishstandards foragentic workflows intelligent search conversational interfaces and data-grounded reasoning across CWs enterprise platforms.

- Shape engineering roadmaps using best-in-class development stacks (e.g. TypeScript/ Python Go C# React microservices event-driven architectures).

- Foster a modern engineering culture emphasizing automation craftsmanship clean architecture and continuous improvement.

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Hands-On Full-Stack & Platform Engineering Leadership

Guide the design and development of solutions built with:

- Frontend: React TypeScript Tailwind micro-frontends

- Backend: Python (FastAPI) Core event-driven microservices

- Cloud & Infra: Azure (preferred) AWS Kubernetes (AKS/EKS) serverless architectures Terraform

- Data & AI:Databricks (Genie/AI-BI Mosaic AI Agent Framework Unity Catalog Delta Lake medallion architecture model serving)Azure Data Lake vector databases LLM orchestration and agentic frameworks

- APIs:GraphQLgRPC REST at scale

Drive innovation by embedding AI/ML automationproduction prompt engineering (system prompt architecture context injection business-rule encodingcitationand grounding patterns)intelligent workflows and data interoperability into core products.

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Execution Quality & Delivery Excellence

Champion excellence through best practices:

- Enterprise-grade CI/CD (Azure DevOpsArgoCD)

- Automated testing contract testing and quality gates

- Observability platforms (PrometheusOpenTelemetry Grafana New Relic Datadog)

- Secure-by-design principles threat modeling zero trust patterns

Oversee engineering delivery across multiple squads ensuring consistency velocity and reliability.

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AI Engineering Standards

Define and enforce AI engineering standards across products and external delivery teams:

- AI evaluation framework design golden-question sets hallucination measurement regression testing and model quality gates for production AI systems

- Pre-semantic data contracts schema stability requirementscanonicalentity ID definitions and data quality thresholds that AI systems can depend on

- AI governance access control patterns for confidential and client data responsible use guardrails and auditability requirements for AI-generated outputs

- LLM system design standards prompt versioning context window management grounding and citation patterns and failure-mode handling

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Architecture Governance & Platform Integrity

Lead architectural reviews and key design decisions across services domains and integrations.

Establish patterns for scalability fault tolerance global data compliance and cost optimization.

Maintainhigh standardsin coding cloud security API governance and data interoperability.

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Stakeholder Alignment & Technical Influence

Partner with Product Architecture Operations and Client Technology to prioritize work unblock teams and drive outcomes.

Represent engineering in executive and governance forums with clarity around architectural options tradeoffs and investment needs.

Translate complex engineering realities into clear business insights that support strategic decision-making.

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What You Bring Technical Background

- Bachelors/Masters in Computer Science Engineering or relatedfield.

- 8 years of software engineering experience and 3 years leading technical teams or engineering managers.

- Deep experience with modern full-stack development cloud-native systems distributed architectures and high-scale platforms.

-Demonstratedexperience designing and shipping production LLM/AI systems: agentic frameworks RAG pipelines text-to-SQL grounding and AI evaluation harnesses.

- Hands-onexpertisewithPython Go TypeScript/JavaScript C# Java SQL/NoSQL; React FastAPI .NET Angular Core microservices Kubernetes

- Strong command of DevOps CI/CD automation infrastructure-as-code and observability.

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Leadership Competencies

- Strategic engineering thinker who balances innovation with disciplined execution.

- Proven ability to mentor engineers grow high-performing teams and attract top talent.

- Ability toinfluenceproduct business and technical leadership.

- Comfortable making architectural decisions that require both technical depth and business awareness.

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Bonus Experience (Highly Attractive)

- Distributed event streaming (Kafka EventHub) CQRS/event-sourcing patterns.

- Experience building platforms used across global organizations.

- Experience technically overseeing external development vendors or contract engineering teams.