Lead AI Forward Engineer

Reuters; Ciudad de México; Híbrido; MXN 900.000 - 1.500.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Hybrid work model

Descripción de la vacante

Refinitiv is seeking a Lead AI Forward Engineer to design and guide AI-powered solutions across the CIO organization. You will partner with teams to identify opportunities, design architectures, and drive implementations from prototype to production.

You will build scalable pipelines, dashboards, and governance for reliability, security, and cost, while mentoring engineers and aligning with product, data science, and security stakeholders. This role features a hybrid work model.

Formación

• 6+ years of experience in solution architecture or senior engineering roles.
• Experience taking AI/ML solutions to production in ambiguous environments.
• Familiarity with LangChain, LlamaIndex, RAG, and enterprise integration considerations.
• Strong DevOps principles and security/compliance awareness.

Responsabilidades

• Identify high-impact AI opportunities across CIO technology teams.
• Design end-to-end AI solutions including data flows and patterns.
• Guide prototype-to-production deployment with reliability and security.
• Establish reusable architectural patterns to enable broader adoption.
• Develop telemetry dashboards and observability for AI services.
• Define and enforce SLOs/SLIs and governance requirements.
• Mentor engineers and align designs with stakeholders.

Conocimientos

Python

LLM frameworks

DevOps / Platform Engineering

Cloud platforms

Descripción del empleo

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.
• 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/SLIs for critical AI services to meet enterprise reliability and compliance requirements.
• Integrate AI observability tooling into CI/CD so new models, prompts, and workflows are automatically enrolled in monitoring and evaluation.
• Develop automated guardrails and policy enforcement (e.g., limits, anomaly detection, abuse/failure pattern detection) with cloud engineering and security teams.
• Partner with engineering teams, service owners, and stakeholders to translate business needs into technical requirements and solution designs.
• Communicate trade‑offs and design decisions clearly to both technical and non‑technical audiences, including senior leadership when needed.
• Mentor engineers and share patterns, practices, and lessons learned to raise overall AI solution design maturity.
• Partner with Product, Data Science and AI teams to design and run evaluation frameworks for LLMs/ML models (offline/online tests, benchmarks, canaries, A/B experiments).
• Work with Product, Data Science, AI Inference Engineering, and Enterprise AI teams to onboard new AI use cases into the observability platform from day one.
• Collaborate with Cloud Engineers (AWS, Azure and GCP) and SREs to align AI observability with broader platform observability and capacity management.
• Support scaling and monitoring of AI infrastructure and workloads during major releases and global events.

Strong solution design/architecture capability:

• end‑to‑end system design, integration patterns, API thinking, and operational design.
• Working knowledge of AI/ML and LLM application patterns, including: LLM capabilities/limitations, prompt design, orchestration approaches, and agent workflows.
• Practical trade‑offs (latency, quality, cost, safety, reliability) Production AI systems and observability challenges (prompting, context windows, RAG, hallucinations, provider variability).
• Proficiency in Python (strongly preferred) and ability to prototype and validate designs with hands‑on technical work.
• Cloud architecture familiarity in AWS, Azure, or GCP, including common service patterns and enterprise constraints.
• Knowledge of distributed systems, microservices, CI/CD, and cloud‑native architectures.
• Strong communication skills: ability to document designs, influence decisions, and align diverse stakeholders.

About you:

• 6+ years of experience with progression in solution architecture, technical strategy, or senior engineering roles.
• Experience building software prototypes and taking solutions to production in ambiguous, low‑precedent environments.
• Familiarity with LLM frameworks and patterns (e.g., LangChain, LlamaIndex), RAG/vector search concepts, and enterprise integration considerations.
• Experience with DevOps/Platform Engineering/SRE principles and designing for operational excellence.
• Exposure to enterprise service management (e.g., ServiceNow/ITSM), security architecture, and compliance‑oriented environments.
• Demonstrated technical leadership through mentoring, architectural governance, or cross‑team enablement.

What’s in it For You?

Hybrid Work Model

We’ve adopted a flexible hybrid working environment for our office‑