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 CW's enterprise platforms.

- Shape engineering roadmaps using best-in-class development stacks (e.g., TypeScript/Node.js, Python, Go, C#, React, Next.js, 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, Next.js, TypeScript, Tailwind, micro-frontends

- Backend: Node.js, Python (FastAPI), Go, .NET 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:GraphQL,gRPC, REST at scale

Drive innovation by embedding AI/ML, automation,production prompt engineering (system prompt architecture, context injection, business-rule encoding,citationand 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 DevOps,ArgoCD)

- Automated testing, contract testing, and quality gates

- Observability platforms (Prometheus,OpenTelemetry, 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 requirements,canonicalentity 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

- Bachelor's/Master's 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, Next.js, Node.js,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.