AI Lead Architect
Merkle (Dentsu Aegis Network) · Posted Oct 8
Data-driven customer experience management, CRM, and digital transformation services
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About the role
The AI delivery practice at Merkle (dentsu) works on enterprise engagements spanning GenAI, Agentic AI, and applied ML, with a focus on frontier AI capabilities and engineering pillars. The role is a strictly hands-on individual contributor and technical leadership position covering deep learning architectures, multimodal agentic systems, SLM design, evaluation standards, and production readiness. The lead architect will drive solution architectures, design reviews, and the technical bar for the practice, while engaging client stakeholders and supporting pre-sales for new GenAI and Agentic AI opportunities.
What you will do
- Lead the hands-on engineering for end-to-end AI solutions across Deep Learning, GenAI, Agentic AI, and multimodal use cases.
- Apply rigorous "fail fast" logic to all AI project management. Quickly identify, evaluate, and disqualify unviable AI use cases based on technical feasibility, effort, cost, and risk early in the cycle.
- Perform explicit trade-off analysis on model class (frontier vs. SLM vs. fine-tuned), retrieval design, memory optimization, and orchestration.
- Lead solutioning, support architecture for end-to-end AI solutions across GenAI, Agentic AI, multimodal, and applied ML use cases, with explicit trade-off analysis on model class (frontier vs. SLM vs. fine-tuned), retrieval design, memory, and orchestration.
- Own the practice's reference architectures and solution design patterns for multimodal agentic systems, including planning, tool use, memory, grounding, and inter-agent communication (MCP, A2A).
- Conduct solution design reviews across concurrent client engagements; facilitate subjective technical decisions and enable delivery excellence.
- Design and lead the build of multi-agent systems with reasoning, planning, tool use, persistent memory, and grounded retrieval.
- Lead multimodal system design and solutions across text, vision, speech, and structured data, including ingestion, representation, and downstream agent reasoning.
- Establish patterns for SLM design and adoption — distillation, fine-tuning, quantization, and routing — to meet enterprise constraints on cost, latency, data residency, and on-prem/edge deployment
- Define hybrid retrieval and knowledge architectures spanning vector, graph (KG), and NoSQL stores; lead KG-assisted retrieval, entity linking, and structured grounding
- Establish evaluation as a first-class discipline: design eval frameworks, golden datasets, regression suites, automated and human-in-the-loop evals, and observability for agentic and generative systems.
- Define and enforce safety, guardrail, and hallucination-control standards across the practice; lead red-teaming and adversarial testing for high-stakes deployments.
- Set the bar for production readiness—reliability, latency, cost, monitoring, drift detection, and incident response—for AI systems in regulated, enterprise-grade environments.
- Lead GPU/accelerator ops, model serving, and lifecycle automation for deployment across cloud hyper-scalers, on-prem, and edge
- Act as a technical sentinel for the AI practice, mentoring engineers through rigorous code and architecture reviews to ensure permanent capability building rather than temporary crisis management.
- Establish and enforce AI in SDLC frameworks on delivery projects
- Engage with client and stakeholder leadership on architecture, feasibility, and risk; communicate technical direction clearly to non-technical audiences.
- Support pre-sales and solutioning for new GenAI and Agentic AI opportunities, including effort estimation, architectural framing, and capability storytelling.
Skills used in this role
What the employer is looking for
- Deep practical grounding in neural networks, Transformers, predictive modeling, embeddings, vector search; CV, NLP, and time series exposure.
- Generative AI: LLMs and SLMs, RAG/Agentic RAG, multimodal architectures, agents, prompt engineering, grounding, knowledge graphs, fine-tuning (SFT, LoRA/QLoRA, RLHF/RLAIF), distillation, and quantization.
- Agentic AI: Multi-agent orchestration, planning, tool use, persistent memory, MCP, and A2A patterns; frameworks such as LangGraph, LlamaIndex, AutoGen.
- Programming & Engineering: Python (advanced), SQL; strong API and backend engineering in FastAPI/Flask/Django; production-grade software practices
- Cloud & Data Engineering: Kafka, Spark/Flink, Hadoop, MongoDB, and other NoSQL/graph/vector stores. Deep experience with AWS, Azure, or GCP
- Math Foundations: Linear algebra, probability, statistics, optimization.
- Minimum 8+ years of total hands-on software development and engineering experience, with a proven track record of reliably deploying solutions on enterprise platforms.
- 3+ years of deep, hands-on experience building and deploying Deep Learning and AI systems in production (within the total 8 years of experience)
- Demonstrable hands-on work in GenAI and/or Agentic AI—beyond basic API wrappers and simple RAG—including multi-agent systems, custom fine-tuning, or SLM-based deployment.
Preferred qualifications
- Experience with commerce cloud ecosystems (Salesforce and Adobe).
Benefits and support
- Compensation and benefits are detailed in the job posting
About Merkle (Dentsu Aegis Network)
Merkle is a leading data-driven customer experience management (CXM) company and a dentsu company that specializes in delivering unique, personalized customer experiences across platforms and devices. Leveraging a deep heritage in data science, technology, and analytics, the agency helps Fortune 1000 brands build holistic, end-to-end customer relationships. Merkle operates globally with thousands of employees across the Americas, EMEA, and APAC.
- Industry
- Marketing
- Company size
- 16000+ employees
- Founded
- 1988
- Location
- Columbia, Maryland, USA
- Funding stage
- Public Company
Funding
Public Company
Leadership
Global President & President, Merkle Americas
Chief Operating Officer, Americas
Chief Technology Officer, Americas
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