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 outlook, 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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Design, develop, and ship production-grade AI applications - including conversational Assistants, RAG pipelines, and multi-agent systems.
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Architect scalable, secure, and cost-efficient backend services using Python, Node.js, and cloud-native patterns (AWS / Azure/ GCP).
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Build and maintain API services (RESTful, streaming) that integrate AI capabilities with enterprise systems.
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Write clean, testable, well-documented code with CI/CD standards; champion engineering rigor in an AI-first team.
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Build and optimize LLM-powered features - including prompt engineering, structured output design, tool/function calling, and context management (multi-turn conversations, session handling).
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Design and implement evaluation frameworks (groundedness scoring, regression testing, quality benchmarking) for AI outputs - ensuring trust, accuracy, and continuous improvement.
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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.
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Design and build enterprise RAG pipelines - including embedding selection, chunking strategies, metadata enrichment, hybrid retrieval, re-ranking, and citation/traceability.
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Integrate and manage vector databases for scalable knowledge retrieval across heterogeneous enterprise data sources.
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Continuously improve retrieval quality by building golden test sets, measuring relevance, and implementing feedback loops.
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Work with Multimodal retrieval based on unstructured content.
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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.
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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.
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Develop reusable tool integrations that agents can invoke autonomously, with proper guardrails and safety controls.
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Work with structured and unstructured enterprise data — cleaning, transforming, and preparing data for AI consumption.
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Apply data science fundamentals (EDA, statistical analysis, anomaly detection) to diagnose issues, validate model behavior, and derive actionable insights from AI system telemetry.
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Collaborate with data engineering teams to ensure data pipelines are reliable, timely, and aligned with AI feature needs.
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Implement Responsible AI practices - including guardrails for hallucination handling, PII protection, restricted topic filtering, and compliance with enterprise security standards.
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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.
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
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Generative AI: Hands-on experience with LLM APIs (Azure OpenAI, AWS Bedrock, Anthropic, Google Gemini); prompt engineering, structured outputs, tool/function calling
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RAG: Proven experience building RAG pipelines - embedding models, chunking, retrieval logic, vector database, re-ranking, and grounding
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Agentic AI: Experience designing agent-based architectures - tool use, planning, multi-step workflows; familiarity with AWS Bedrock, Azure AI Foundry, or equivalent frameworks
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Data Fundamentals: Solid understanding of data wrangling, SQL, EDA, and basic ML concepts; ability to work with structured/unstructured data at scale
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Data Bricks: Designs, builds, and manages scalable data pipelines and AI solutions within the Databricks Lakehouse Platform.
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Cloud: Experience with AWS or Azure - deploying containerized services, serverless functions, and working with cloud AI/ML services
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System Design: Ability to design distributed, scalable AI systems with clear tradeoffs on cost, latency, and reliability
Good-to-Have / Forward-Looking Skills
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Multi-Agent Systems & A2A Protocols - experience with agent-to-agent communication patterns, Model Context Protocol (MCP), or similar emerging standards.
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Fine-Tuning & Model Adaptation - experience fine-tuning LLMs or adapter-based methods (LoRA, QLoRA) for domain-specific use cases.
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AI Evaluation & Benchmarking - experience building evaluation harnesses, automated grading, and regression testing for LLM outputs.
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Microsoft Ecosystem - familiarity with M365 Copilot, Copilot Studio, Bot Framework, Teams integrations, Adaptive Cards.
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Observability & Tracing - experience with AI-specific observability for debugging and monitoring AI systems in production.
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NLP & Classical ML - deeper grounding in NLP (named entity recognition, text classification, sentiment analysis) and classical ML (scikit-learn, XGBoost).
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Knowledge Graphs & Hybrid Search - experience combining graph-based retrieval with vector search for richer contextual grounding.
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Edge / Cost Optimization - techniques for reducing inference cost, including model distillation, quantization, caching, and batching strategies.
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Security & Compliance - awareness of data privacy regulations, secure API design, and AI red-teaming / adversarial testing.
What Sets You Apart
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You think like a software engineer first - you care about clean code, testable systems, and operational excellence - and you apply that rigor to AI systems.
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You have a builder's attitude - you're not just consuming APIs; you're designing platforms, building reusable components, and thinking about how your work scales to 50+ teams.
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You bring data intuition - you can EDA your way through a problem, validate model behavior with data, and explain tradeoffs with metrics.
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You're curious and forward-looking - you track the evolving landscape of AI agents, evaluation, and orchestration and bring those ideas to the team.
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You thrive in a fast-paced, collaborative environment where you work closely with product, operations, and leadership to deliver measurable impact.
Education:
Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field (or equivalent practical experience).
