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.

Learn more about Publicis Re:Sources and the Publicis Groupe agencies we support at

@publicisresources.com

Title: AI ML Engineer (Senior Associate)

Location: Bangalore, Pune, Gurugram.

Role Summary 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

Key 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 systems o Large Language Models (LLMs) and multimodal AI (text, image, audio)

o Retrieval-Augmented Generation (RAG)



and enterprise knowledge systems o Recommendation and personalization engines o Agentic AI and intelligent automation o 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 OpenAI o 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 architectures o 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 tools o Content, document, or knowledge management systems o 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 monitoring o Versioning, lineage, auditability, and rollback

· Drive adoption of MLOps and LLMOps best practices, including:

o CI/CD for models, prompts, and pipelines o Monitoring for drift, bias, latency, and hallucinations o 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 regulations o Industry and organizational compliance requirements o Responsible AI principles (fairness, transparency, explainability)

· Leverage Azure security and governance capabilities, including:

o Identity and access management o Data protection and encryption o 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 AI o Agent-based workflows and orchestration frameworks o 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, adapters o Multimodal AI and RAG architectures o 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.

· Robust understanding of distributed systems, cloud security, and data engineering.

Preferred Skills:

· 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.

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 field

📌 Senior Associate AI ML Engineer (Karnataka)
🏢 Publicis Re:Sources
📍 Karnataka