Design and build applied AI solutions that drive measurable business value from concept through scalable production deployment, architecting enterprise AI systems leveraging large language models, retrieval-augmented generation, and agentic workflows while leading technical strategy and mentoring engineering teams.
Responsibilities
Design and architect AI-powered systems using LLMs, RAG, agentic workflows, and orchestration patterns integrated with enterprise data and business processes
Develop secure, maintainable, production-ready software platforms and cloud-native services that orchestrate models, tools, retrieval systems, and enterprise workflows
Build rapid prototypes and proof-of-concepts to validate new technologies and identify business opportunities
Establish comprehensive evaluation, monitoring, and quality practices including testing, benchmarking, observability, and continuous improvement
Lead technical design discussions, architecture reviews, and drive engineering best practices across teams
Requirements
Bachelor's degree in computer science, Software Engineering, AI, Data Science, or a related field
Nice to have: MLOps/LLMOps practices
Nice to have: Terraform
Nice to have: Helm Charts
Nice to have: LLM Serving & AI Platforms (vLLM, LiteLLM, KServe)
Lead the design and build of an evaluation framework for an enterprise planner-based agent, benchmarking agents across planner models, executor LLMs, and inference optimization levers.
Compensation
Our salary range is $200,00 - $300,000
Responsibilities
Design the end-to-end evaluation workflow for a planner-executor agent: scenarios, user simulation, configuration, execution, grading, comparison and insights
Define the scenario schema, grading spec and validators with the client's team
Build an LLM-based user simulator with a hidden goal and a stop rule that holds realistic multi-turn conversations
Build isolated, parallel experiment execution
Package optimization levers as plug-ins and implement a shortlist of 3 to 4 for v1
Requirements
Nice to have: Publications or open-source contributions in LLM evaluation, agents or efficient inference
Nice to have: Building internal dashboards or web UIs that a client team can adopt as their own
Nice to have: Hands-on use of an LLM gateway or tracing stack (LangFuse or LangSmith)
To design, develop and improve software, utilising various engineering methodologies, that provides business, platform, and technology capabilities for our customers and colleagues.
Responsibilities
Build and evolve model deployment pipelines, registries, and monitoring frameworks.
Implement and optimize SageMaker, Databricks, MLflow, and related orchestration tools.
Ensure compliance with model governance, auditability, and AI safety standards.
Drive reproducibility, lineage tracking, drift detection, and rollback strategies.
Write production-grade Python code for automation, orchestration, and monitoring.
Senior Python Engineer responsible for designing, building, and scaling backend systems that power AI across the platform.
Locations (2)
Hanoi, Hanoi, Vietnam
Ho Chi Minh City, Ho Chi Minh City (HCMC), Vietnam
Responsibilities
Design, implement, and maintain robust Python/FastAPI microservices and backend systems that integrate LLMs and generative AI capabilities directly into live workflows.
Build automated pipelines and tooling to continuously measure, evaluate, and improve LLM output accuracy, consistency, latency, and safety.
Develop internal libraries, abstractions, and standardized patterns that empower engineering squads to ship AI features quickly and reliably.
Optimize AI services and inference pipelines for throughput, latency, token economics, caching, and high availability.
Partner with the CTO and technical leads to evaluate emerging AI technologies, architect retrieval-augmented generation (RAG) pipelines, and design vector search infrastructure.
Associate Director, Software Engineering (Guardrail Platform AI Safety Track)
Lead the design, development, and production of AI safety detectors and guardrail solutions for enterprise AI applications.
Locations (3)
Guangzhou, Guangdong, China
Xi'an, Shaanxi, China
Shenzhen, Guangdong, China
Responsibilities
Design, develop and productionise AI safety detectors for enterprise AI applications
Build detector pipelines using rule‑based detection, pattern matching, NLP/ML classifiers, embedding models, LLM‑as‑a‑Judge evaluators, and hybrid detection strategies
Develop and optimise detector models and evaluation workflows, including test set creation, data labeling, fine‑tuning, model validation, threshold calibration, precision/recall optimisation, regression testing, and continuous improvement
Evaluate and integrate industry and open‑source AI safety solutions, perform build‑vs‑buy analysis, benchmarking, integration, and production readiness assessment
Help finance teams build models they trust and run planning processes that work across their organization, shaping how customers connect business data, build budgets and forecasts, collect contributions, and turn changing assumptions into decisions using AI agents.
Locations (3)
Germany
Spain
United Kingdom
Responsibilities
Build agent prototypes with AI coding tools and work with engineers to ship reliable Modeler Agent capabilities
Define agent evaluations with golden datasets and expected numerical results, and investigate failures through traces and customer feedback
Own the roadmap for your area of xP&A, trading off customer needs, adoption, reliability and commercial impact
Make financial accuracy testable by checking formulas, aggregations, actuals versus forecasts, overrides and scenarios
Help finance teams build models they trust and run planning processes that work across their organization, shaping how customers connect business data, build budgets and forecasts, collect contributions, and turn changing assumptions into decisions, with AI agents being central to the work.
Locations (3)
Germany, Germany
Spain, Spain
United Kingdom, United Kingdom
Responsibilities
Build agent prototypes with AI coding tools and work with engineers to ship reliable Modeler Agent capabilities
Define agent evaluations with golden datasets and expected numerical results, and investigate failures through traces and customer feedback
Own the roadmap for your area of xP&A, trading off customer needs, adoption, reliability and commercial impact
Make financial accuracy testable by checking formulas, aggregations, actuals versus forecasts, overrides and scenarios
The role is pivotal in software development activities and collaboration across the Strategy & Transformation (S&T) organization, building highly scalable distributed solutions and applied AI solutions (AI agents, RAG and document/vision/speech models).
Responsibilities
Support the vision and guide a team of engineers to build and support digital products and services (DPS) across S&T core priorities
Drive partnerships with User Experience, Product Management, IT, Data & Analytics, Emerging Tech, Innovation, and Process Engineering teams to deliver the Digital Products portfolio
Develop software development strategy utilizing industry standards. Create roadmap and timing of implementation of the roadmap based on business requirements and strategy.
Lead multi-discipline, high-performance work teams distributed across remote locations effectively. Build, manage, develop, and mentor a team of engineers.
Interact with executives across the company to lead the narrative around software engineering
Requirements
Bachelor’s Degree in Computer Science or a related field
Design end‑to‑end AI safety protection patterns across enterprise AI workloads, including RAG systems, coding assistants, conversational AI and agent applications
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