Job Description

About the Role

We are looking for a Senior AI Engineer to join our AI Center of Excellence team at Epsilon, working at the heart of enterprise-grade Conversational, Agentic, and AI Platforms and applications. This role is for someone who brings a strong software engineering backbone, a data oriented perspective, and deep hands-on expertise in Generative AI, RAG, and Agentic AI systems and is ready to help shape the future of intelligent, enterprise systems at scale.

You will be building and scaling AI-powered applications and agents that serve thousands of associates and clients across Epsilon and Publicis Groupe, integrating with enterprise systems. You will own and operate across the full lifecycle - from ideation, experimentation and prototyping to production hardening, evaluation, and operational governance.

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Responsibilities

Core Engineering & Architecture

• Design, develop, and ship production-grade AI applications - including conversational Assistants, RAG pipelines, and multi-agent systems.
• Architect scalable, secure, and cost-efficient backend services using Python, Node.js, and cloud-native patterns (AWS / Azure/ GCP).
• Build and maintain API services (RESTful, streaming) that integrate AI capabilities with enterprise systems.
• Write clean, testable, well-documented code with CI/CD standards; champion engineering rigor in an AI-first team.

Generative AI & LLM Systems

• Build and optimize LLM-powered features - including prompt engineering, structured output design, tool/function calling, and context management (multi-turn conversations, session handling).
• Design and implement evaluation frameworks (groundedness scoring, regression testing, quality benchmarking) for AI outputs - ensuring trust, accuracy, and continuous improvement.
• Stay hands-on with LLM APIs (Azure OpenAI, AWS Bedrock, Anthropic, open-source models) and make informed decisions on model selection, cost-latency tradeoffs, and fine-tuning vs. prompting strategies.

Retrieval-Augmented Generation (RAG)

• Design and build enterprise RAG pipelines - including embedding selection, chunking strategies, metadata enrichment, hybrid retrieval, re-ranking, and citation/traceability.
• Integrate and manage vector databases for scalable knowledge retrieval across heterogeneous enterprise data sources.
• Continuously improve retrieval quality by building golden test sets, measuring relevance, and implementing feedback loops.
• Work with Multimodal retrieval based on unstructured content.

Agentic AI & Orchestration

• Design and implement agentic workflows - autonomous and semi-autonomous AI agents that can reason, plan, use tools, and implement multi-step business workflows with human-in-the-loop checkpoints.
• Build multi-agent orchestration frameworks using tools like AWS Bedrock, Agentcore, Cursor and other state-of-the-art open-source frameworks - enabling collaborative agent systems for complex enterprise scenarios.
• Develop reusable tool integrations that agents can invoke autonomously, with proper guardrails and safety controls.

Data & Analytics Mindset

• Work with structured and unstructured enterprise data — cleaning, transforming, and preparing data for AI consumption.
• Apply data science fundamentals (EDA, statistical analysis, anomaly detection) to diagnose issues, validate model behavior, and derive actionable insights from AI system telemetry.
• Collaborate with data engineering teams to ensure data pipelines are reliable, timely, and aligned with AI feature needs.

Governance, Safety & Ops

• Implement Responsible AI practices - including guardrails for hallucination handling, PII protection, restricted topic filtering, and compliance with enterprise security standards.
• Build and operate LLMOps / MLOps pipelines - model deployment, monitoring, logging, tracing, cost tracking, and lifecycle management.
• Contribute to SoPs, governance documentation, and operational runbooks for AI systems deployed across teams.

Qualifications

Must-Have Skills & Experience:

• Experience: 5–8+ years in software engineering, with at least 2+ years hands-on in Generative AI / LLM-based systems
• Software Engineering: Strong proficiency in Python; experience with backend frameworks (FastAPI, Flask, Express/Node.js); clean API design, version control (Git), testing, and CI/CD
• Generative AI: Hands-on e…