Us And Help Shape
Technology
LeadAIForwardEngineer
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“Lead AI Forward Engineer at Us And Help Shape. Skills: AI Forward Engineering, Solution Architecture, AI/ML Technologies, System Design. Design AI-powered solutions. Guide AI solution delivery”
What You'll Achieve.
Reduce operational toil; Accelerate technology teams; Ensure solutions are scalable; Ensure solutions are maintainable; Ensure solutions are extensible; Enable broader adoption; Meet enterprise reliability requirements; Meet enterprise compliance requirements; Raise AI solution design maturity
Industry & Context.
Identify opportunities; Design end-to-end architectures; Drive implementations; Evaluate AI/ML technologies; Apply sound judgment; Root cause analysis
What They're Looking For.
Must Have
6+ years experience, Build software prototypes, Take solutions to production, Python proficiency, Cloud architecture familiarity, Distributed systems knowledge, Microservices knowledge, CI/CD knowledge, Cloud-native architectures knowledge
Nice to Have
LangChain familiarity, LlamaIndex familiarity, RAG/vector search concepts familiarity, Enterprise integration considerations familiarity, DevOps/Platform Engineering/SRE principles experience, Designing for operational excellence experience, Enterprise service management exposure, Security architecture exposure, Compliance-oriented environments exposure, Technical leadership demonstrated
What You'll Do.
Design AI-powered solutions
Guide AI solution delivery
Reduce operational toil
Accelerate technology teams
Operate as solution architect
Identify opportunities
Design end-to-end architectures
Drive implementations to production
Ensure solutions are scalable
Ensure solutions are maintainable
Ensure solutions are extensible
Evaluate emerging AI technologies
Define repeatable patterns
Build new capabilities
Hands-on implementation
Identify high-impact opportunities
Apply intelligent agents
Design integration patterns
Design operational considerations
Guide implementation from prototype
Guide implementation to production
Ensure solutions meet reliability
Ensure solutions meet security
Ensure solutions meet compliance
Define reusable architectural patterns
Define reference designs
Enable broader adoption
Build scalable pipelines
Collect inference telemetry
Analyze inference telemetry
Collect workflow telemetry
Analyze workflow telemetry
Integrate with data backbone
Provide visibility into performance
Provide visibility into reliability
Provide visibility into safety
Provide visibility into cost
Ensure compliance with AI standards
Ensure AI auditability
Evaluate AI/ML technologies
Recommend AI/ML technologies
Evaluate AI/ML platforms
Recommend AI/ML platforms
Design flexible architectures
Evolve architectures with model changes
Evolve architectures with provider changes
Evolve architectures with emerging AI
Meet enterprise reliability requirements
Meet enterprise compliance requirements
Integrate AI observability tooling
Enroll models in monitoring
Enroll prompts in monitoring
Enroll workflows in monitoring
Enroll models in evaluation
Enroll prompts in evaluation
Enroll workflows in evaluation
Develop automated guardrails
Develop policy enforcement
Partner with engineering teams
Partner with service owners
Partner with stakeholders
Translate business needs
Translate technical requirements
Communicate trade-offs
Communicate design decisions
Share lessons learned
Raise AI solution design maturity
Partner with Product teams
Partner with Data Science teams
Partner with AI teams
Design evaluation frameworks
Run evaluation frameworks
Design ML model tests
Design A/B experiments
Partner with AI Inference Engineering
Partner with Enterprise AI teams
Onboard new AI use cases
Collaborate with Cloud Engineers
Collaborate with SREs
Align AI observability
Align platform observability
Align capacity management
Support scaling AI infrastructure
Support scaling AI workloads
Support monitoring AI infrastructure
Support monitoring AI workloads
Build software prototypes
Take solutions to production
Experience building software prototypes
Experience taking solutions to production
Experience with DevOps principles
Experience with Platform Engineering principles
Experience with SRE principles
Design for operational excellence
Support scaling infrastructure
Support monitoring workloads
Support scaling workloads
Support monitoring infrastructure
How You'll Work.
Team & Collaboration
Partnering closely with teams; Cross Functional Partnership; Partner with engineering teams; Partner with service owners; Partner with stakeholders; Partner with Product teams; Partner with Data Science teams; Partner with AI teams; Collaborate with Cloud Engineers; Collaborate with SREs; Cross-team enablement
Communication Scope
Communicate trade-offs; Communicate design decisions; Document designs; Influence decisions; Align diverse stakeholders
Process & Methodology
DevOps, Platform Engineering, SRE
Full Job Description
The Lead AI Forward Engineer designs and guides the delivery of AI-powered solutions that reduce operational toil and accelerate technology teams across the CIO organization. This role operates as a forward-deployed solution architect and engineer, partnering closely with teams to identify opportunities, design end-to-end architectures, and drive implementations to production. You will own solution design from concept through deployment, ensuring solutions are scalable, maintainable, and extensible. You will evaluate emerging AI technologies, define repeatable patterns, and help build new capabilities through hands-on implementation, mentorship, and shared standards. **About the Role** **Solution Architecture and Delivery** * Identify high-impact opportunities to apply AI automation and intelligent agents across CIO technology teams. * Design end-to-end AI solutions, including workflows, integration patterns, data flows, and operational considerations. * Guide implementation from prototype to production, ensuring solutions meet reliability, security, and compliance expectations. * Define reusable architectural patterns and reference designs to enable broader adoption across teams. * Build scalable pipelines to collect and analyze inference‑ and workflow‑level telemetry, integrating with TR's data backbone. * Develop dashboards and reports providing clear visibility into performance, reliability, safety, and cost. * Ensure compliance with TR's AI standards for monitoring, governance, privacy, and auditability. **AI System Design and Technology Strategy** * Evaluate and recommend AI/ML technologies and platforms (LLM orchestration, agentic frameworks, cloud AI services) based on capability, cost, risk, and fit. * Design flexible architectures that can evolve with model/provider changes and emerging AI capabilities. * Apply sound judgment on when AI is appropriate vs. when simpler automation or traditional engineering approaches are better. * Establish and track SLOs/S
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