Senior Associate AI/ML Engineer

Company Description

Publicis Re:Sources is at the core of Publicis Groupe, the world's largest communications company. We are the only full-service, end-to-end shared service organization in the industry, enabling Groupe agencies to do what they do best: innovate and transform for their clients.

Formed in 1998 as a small team to service a few Publicis Groupe firms, Publicis Re:Sources has grown to 6,000+ employees in over 55 countries. We provide technology solutions and business services, including finance, accounting, legal, benefits, procurement, tax, real estate, treasury and risk management, information security, and global mobility — supporting 110,000+ employees across the Publicis Groupe network. Our people are at the center of everything we do, bringing curiosity, collaboration, and a commitment to excellence to their work every day. 

Overview

The AI Senior Associate is responsible for defining, governing, and scaling the enterprise AI vision with a strong focus on the Microsoft Azure AI ecosystem. This role leads the design & implementation of secure, scalable and production-grade AI systems across machine learning, generative AI, multimodal AI, and agent-based automation.The Senior Associate partners with business, engineering, data, and platform teams to ensure AI solutions are cloud-native, compliant and aligned with long-term enterprise strategy.This engineer owns model development, pipeline implementation, optimization, and deployment, while contributing to MLOps practices and mentoring junior team members.

Responsibilities

1. AI Strategy & Enterprise Architecture· Help Define and own the enterprise AI architecture roadmap with emphasis on Azurenative services, covering:o Traditional ML and Deep Learning systemso Large Language Models (LLMs) and multimodal AI (text, image, audio)o Retrieval-Augmented Generation (RAG) and enterprise knowledge systemso Recommendation and personalization engineso Agentic AI and intelligent automationo Responsible and compliant AI solutions· Translate business and domain requirements into Azure-aligned AI reference architectures.· Establish architectural standards, reusable patterns, and best practices for AI adoption across the organization.2. Azure Platform & Infrastructure Architecture· Design and Develop AI applications on Azure-based AI platforms, including:o Azure AI Studio and Azure OpenAIo Azure Machine Learning (training, pipelines, feature stores)o GPU/accelerator-backed compute (Azure VM SKUs, AKS)o Azure Data Lake, Synapse, Fabric, or equivalent lakehouse architectureso Vector databases (Azure AI Search, third-party integrations)· Define scalable ingestion and processing pipelines for high-volume and real-time data.· Help Architect integrations with enterprise systems such as:o Data platforms and analytics toolso Content, document, or knowledge management systemso Event-driven architectures, APIs, and observability platforms· Ensure solutions meet performance, availability, cost, and security objectives.3. Model Lifecycle, MLOps & LLMOps· Define & implement end-to-end model lifecycle management using Azure-native and open-source tools:o Training, fine-tuning, evaluation, deployment, and monitoringo Versioning, lineage, auditability, and rollback· Drive adoption of MLOps and LLMOps best practices, including:o CI/CD for models, prompts, and pipelineso Monitoring for drift, bias, latency, and hallucinationso Secure prompt management and inference governance· Build shared AI platforms and reusable components to accelerate enterprise AI delivery.4. Governance, Security & Responsible AI· Ensure AI systems comply with:o Data privacy and security regulationso Industry and organizational compliance requirementso Responsible AI principles (fairness, transparency, explainability)· Leverage Azure security and governance capabilities, including:o Identity and access managemento Data protection and encryptiono Policy enforcement and monitoring· Define guardrails for safe AI usage, IP protection, and risk mitigation.5. Innovation & Technical Leadership· Continuously evaluate emerging Azure AI capabilities and ecosystem tools.· Drive experimentation and adoption of:o Generative and multimodal AIo Agent-based workflows and orchestration framewworkso Advanced inference optimization and deployment strategies· Act as a technical thought leader and advisor to senior leadership.· Present AI architecture strategies, trade-offs, and roadmaps to executive stakeholders.

Required Skills & Expertise:

· Deep expertise in enterprise AI/ML development and system design.· Strong hands-on experience with:o Large Language Models (LLMs), embeddings, fine-tuning, adapterso Multimodal AI and RAG architectureso Vector search and semantic retrieval· Expert-level experience with Microsoft Azure, including Azure AI and data services.· Proven track record implementing MLOps and LLMOps at scale.· Strong understanding of distributed systems, cloud security, and data engineering.

Qualifications

· 8+ years of experience in AI/ML, data platforms, or advanced analytics.· Minimum 3+ years in Senior / Principal Engineer, or equivalent role.· Bachelor’s degree in Computer Science, Engineering, or related fiel

Additional Information

Experience with generative AI (text, image, audio, or video).· Background in building AI platforms, Centers of Excellence (CoE), or shared services.· Exposure to real-time or large-scale enterprise data systems.· Familiarity with ServiceNow or Other ITSM platforms & Use cases.