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Thomson Reuters

Legal, financial data, and media services

Lead AI Forward Engineer

AI Application EngineeringConfirmed live in the last 24 hours
Full-timeBengaluru, Karnataka, IndiaNo salary listed

About the Job

Requirements

  • Strong solution design and architecture capability, including end-to-end system design, integration patterns, API thinking, and operational design.
  • Working knowledge of AI/ML and LLM application patterns, including LLM capabilities and limitations, prompt design, orchestration approaches, and agent workflows.
  • Understanding of practical AI trade-offs involving latency, quality, cost, safety, and reliability.
  • Understanding of production AI systems and observability challenges, including prompting, context windows, retrieval-augmented generation, hallucinations, and provider variability.
  • Proficiency in Python is strongly preferred, with the ability to prototype and validate designs through hands-on technical work.
  • Familiarity with cloud architecture in AWS, Azure, or GCP, including common service patterns and enterprise constraints.
  • Knowledge of distributed systems, microservices, continuous integration and continuous delivery, and cloud-native architectures.
  • Ability to document designs, influence decisions, and align diverse stakeholders.
  • 10+ 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.
  • Experience with DevOps, Platform Engineering, or SRE principles and designing for operational excellence.
  • Exposure to enterprise service management such as ServiceNow or ITSM, security architecture, and compliance-oriented environments.
  • Demonstrated technical leadership through mentoring, architectural governance, or cross-team enablement.

Responsibilities

  • 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 Thomson Reuters' data backbone.
  • Develop dashboards and reports that provide visibility into performance, reliability, safety, and cost.
  • Ensure compliance with AI standards for monitoring, governance, privacy, and auditability.
  • Evaluate and recommend AI/ML technologies and platforms, including LLM orchestration, agentic frameworks, and cloud AI services, based on capability, cost, risk, and fit.
  • Design flexible architectures that can evolve with model or provider changes and emerging AI capabilities.
  • Determine when AI is appropriate and when simpler automation or traditional engineering approaches are preferable.
  • Establish and track SLOs and 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, such as limits, anomaly detection, and abuse or 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 to technical and non-technical audiences, including senior leadership when needed.
  • Mentor engineers and share patterns, practices, and lessons learned to improve AI solution design maturity.
  • Partner with Product, Data Science, and AI teams to design and run evaluation frameworks for LLMs and ML models, including offline and online tests, benchmarks, canaries, and A/B experiments.
  • Work with Product, Data Science, AI Inference Engineering, and Enterprise AI teams to onboard AI use cases into the observability platform from day one.
  • Collaborate with Cloud Engineers using AWS, Azure, and GCP, and with 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.

Desired Qualifications

  • Familiarity with LLM frameworks and patterns such as LangChain and LlamaIndex, retrieval-augmented generation, vector search concepts, and enterprise integration considerations.
  • Python proficiency is strongly preferred.

About the Company

Thomson Reuters

Legal, financial data, and media services

Thomson Reuters provides intelligent information and professional services for law, tax, finance, and media. It serves law firms, corporations, government agencies, and financial institutions with platforms like legal research (Westlaw), tax software (UltraTax CS), financial market data, and news services. These products are accessed through subscription-based platforms, ensuring ongoing updates and a steady revenue stream. The company also monetizes through advertising on media platforms and data analytics sales. It distinguishes itself by offering a broad, integrated suite of industry-specific tools combined with AI, cognitive computing, and machine learning to improve accuracy, efficiency, and predictive analytics in research and data insights. Its goal is to be a trusted partner that helps professionals make informed decisions by delivering reliable information and decision-ready tools while pursuing responsible, values-driven initiatives.

Company size
10,001+
Company stage
IPO
Headquarters
Toronto, Canada
Founded
1851

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