AI Engineer
dentsuaegis.wd3 · Posted Nov 2
Global marketing, media planning, digital communications, and business transformation services
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About the role
The role is within Dentsu/Merkle and partners with engineering, data science, and CX teams to build AI solutions for customer engagement. It focuses on generative AI, multimodal systems, agentic frameworks, retrieval, and production-grade ML deployment. The AI Engineer will build and deploy AI applications, APIs, and models, and mentor junior engineers.
What you will do
- Build conversational and non-conversational, multimodal, and agentic AI applications using LLMs and frameworks such as LangChain, LangGraph, LlamaIndex, AutoGen, or similar.
- Design AI workflows incorporating reasoning, planning, tool-use, memory, grounding, and external system integrations.
- Develop Knowledge Graph (KG)-assisted AI systems, including entity extraction, linking, and KG-augmented retrieval.
- Ensure safety, consistency, and hallucination-control through structured evaluation and guardrails.
- Transform models into scalable APIs and microservices using Python, FastAPI/Flask, Docker.
- Deploy and monitor ML/AI systems in AWS/Azure/GCP, optimizing for cost, latency, and reliability.
- Collaborate with MLOps teams on CI/CD pipelines, model versioning, monitoring, and automated evaluation.
- Work with big data technologies including Apache Spark, Hadoop, and NoSQL databases such as MongoDB.
- Build and optimize transformer-based and multimodal models using deep learning frameworks (e.g., PyTorch, TensorFlow).
- Implement fine-tuning, alignment (RLHF/RLAIF), LoRA/QLoRA, pruning, and model evaluation pipelines.
- Develop information retrieval systems, including hybrid dense–sparse retrieval, ranking, knowledge graphs, and relevance optimization.
- Build predictive models and ML pipelines from scratch, including data preparation, feature engineering, and model selection.
- Work cross-functionally with CX, engineering, and product stakeholders to translate business needs into AI solutions.
- Document models, experiments, evaluation frameworks, and deployment processes.
- Mentor junior engineers and contribute to internal best practices, reusable components, and R&D initiatives.
Skills used in this role
What the employer is looking for
- Programming: Python (advanced), SQL; robust experience with API development and data engineering,
- Backend Frameworks: Flask, FASTAPI, Django
- Machine Learning: Predictive modelling, deep learning, optimization, embeddings, vector search, model evaluation.
- Generative AI: LLMs, RAG, multimodal architectures, agents, prompt engineering, grounding, knowledge graphs.
- Cloud Platforms: AWS, Azure, or GCP with hands-on experience deploying and scaling AI systems.
- Data Technologies: Apache Spark, Hadoop, MongoDB; strong understanding of data pipelines and large-scale processing.
- Math Foundations: Linear algebra, probability, statistics.
- Minimum 3-4 years of hands-on software development experience including building and deploying machine learning models into production.
- 2+ years of experience working with deep learning, GenAI, or transformer-based architectures.
- Demonstrated experience building GenAI applications beyond simple RAG (e.g., agents, multimodal, custom LLM fine-tuning).
- Experience integrating AI systems in enterprise-grade environments.
- Transformers & Deep Learning: Understands transformers; fine-tunes small models.
- Generative AI (LLMs & Multimodal): Works with LLM APIs; simple RAG and KAG – Demonstrated by project experience
- Information Retrieval & Relevance: Uses vector DBs for retrieval, knowledge graphs - – Demonstrated by project experience
- Predictive Modeling: Builds and deploys ML models; applies evaluation metrics – Demonstrated by project experience
- Knowledge Graphs: Integrates and retrieves from existing KGs
- Conversational AI: Text-only chatbots; intent models.
- Agentic Frameworks: Basic agent/tool calling.
- Model Deployment: Deploys models as basic APIs.
- Cloud & MLOps: Uses cloud AI services.
- Big Data & Pipelines: Writes SQL/Python ETL.
- Deep Learning: Understand and applied deep learning architectures – RNNs, LSTMs, Transformers
- Growth-oriented, collaborative, and experimentation-driven.
- Strong problem-solving skills with a bias toward action.
- Ability to communicate complex concepts clearly to non-technical stakeholders.
- Open and flexible towards a hybrid work structure with no less than 2-days work from office – This is to ensure that the team working in the AI domain regularly connects and does knowledge exchange across projects
Benefits and support
- Compensation and benefits are detailed in the job posting
About dentsuaegis.wd3
Dentsu is a global integrated growth and transformation partner that helps brands navigate disruption and drive sustainable business growth through deep audience insights, modern creativity, media, and technology. Operating across approximately 120 countries, the network unites specialized agencies to deliver transformative progress in marketing, data, and commerce. The company previously operated as Dentsu Aegis Network before unifying its global branding under Dentsu.
- Industry
- Advertising
- Company size
- 67000+ employees
- Founded
- 1901
- Location
- Tokyo, Japan
- Funding stage
- Public Company
Leadership
Takeshi Sano
Representative Executive Officer, President & Global CEO
Shigeki Endo
Executive Officer, Global CFO
Yoshimasa Watahiki
Representative Executive Officer, EVP, Global Chief Corporate Affairs Officer
Shirli Zelcer
Chief Data & Technology Officer
Alex Bedier
Global Chief Information Officer
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