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.
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.
· Strong 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

Work arrangement
Hybrid