Senior Software Engineer

Overview

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 mindset, 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.

Click here to view how Epsilon transforms marketing with 1 View, 1 Vision and 1 Voice.

Responsibilities

What You'll Do

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 innovative 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 experience with LLM APIs (Azure OpenAI, AWS Bedrock, Anthropic, Google Gemini); prompt engineering, structured outputs, tool/function calling
• RAG: Proven experience building RAG pipelines - embedding models, chunking, retrieval logic, vector database, re-ranking, and grounding
• Agentic AI: Experience designing agent-based architectures - tool use, planning, multi-step workflows; familiarity with AWS Bedrock, Azure AI Foundry, or equivalent frameworks
• Data Fundamentals: Solid understanding of data wrangling, SQL, EDA, and basic ML concepts; ability to work with structured/unstructured data at scale
• DataBricks: Designs, builds, and manages scalable data pipelines and AI solutions within the Databricks Lakehouse Platform.
• Cloud: Experience with AWS or Azure - deploying containerized services, serverless functions, and working with cloud AI/ML services
• System Design: Ability to design distributed, scalable AI systems with clear tradeoffs on cost, latency, and reliability

Good-to-Have / Forward-Looking Skills

• Multi-Agent Systems & A2A Protocols - experience with agent-to-agent communication patterns, Model Context Protocol (MCP), or similar emerging standards.
• Fine-Tuning & Model Adaptation - experience fine-tuning LLMs or adapter-based methods (LoRA, QLoRA) for domain-specific use cases.
• AI Evaluation & Benchmarking - experience building evaluation harnesses, automated grading, and regression testing for LLM outputs.
• Microsoft Ecosystem - familiarity with M365 Copilot, Copilot Studio, Bot Framework, Teams integrations, Adaptive Cards.
• Observability & Tracing - experience with AI-specific observability for debugging and monitoring AI systems in production.
• NLP & Classical ML - deeper grounding in NLP (named entity recognition, text classification, sentiment analysis) and classical ML (scikit-learn, XGBoost).
• Knowledge Graphs & Hybrid Search - experience combining graph-based retrieval with vector search for richer contextual grounding.
• Edge / Cost Optimization - techniques for reducing inference cost, including model distillation, quantization, caching, and batching strategies.
• Security & Compliance - awareness of data privacy regulations, secure API design, and AI red-teaming / adversarial testing.

What Sets You Apart

• 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.
• You have a builder's perspective - you're not just consuming APIs; you're designing platforms, building reusable components, and thinking about how your work scales to 50+ teams.
• You bring data intuition - you can EDA your way through a problem, validate model behavior with data, and explain tradeoffs with metrics.
• You're curious and forward-looking - you track the evolving landscape of AI agents, evaluation, and orchestration and bring those ideas to the team.
• 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).

Additional Information

Epsilon is a global data, technology and services company that powers the marketing and advertising ecosystem. For decades, we’ve provided marketers from the world’s leading brands the data, technology and services they need to engage consumers with 1 View, 1 Vision and 1 Voice. 1 View of their universe of potential buyers. 1 Vision for engaging each individual. And 1 Voice to harmonize engagement across paid, owned and earned channels.

Epsilon’s comprehensive portfolio of capabilities across our suite of digital media, messaging and loyalty solutions bridge the divide between marketing and advertising technology. We process 400+ billion consumer actions each day using advanced AI and hold many patents of proprietary technology, including real-time modeling languages and consumer privacy advancements. Thanks to the work of every employee, Epsilon has been consistently recognized as industry-leading by Forrester, Adweek and the MRC. Epsilon is a global company with more than 9,000 employees around the world.

Our pillars aren't just words. They're how we show up every day.

• People centricity: We focus on employee well-being in an environment where colleagues truly care about each other.
• Collaboration: We work together, support one another, and collectively achieve goals.
• Growth: There are endless opportunities for growth through learning, development and career advancement.
• Innovation: We drive progress through cutting-edge solutions and forward-thinking approaches.
• Flexibility: We’ve created a balance between work and personal life, and we encourage adaptability to solve problems creatively.

Our values guide us to create value for our clients, our people and consumers.

• Act with integrity
• Work together to win together
• Innovate with purpose
• Respect all voices
• Empower with accountability

These pillars and values are our foundation—shaping our culture, guiding our decisions, and uniting us in common purpose.

Epsilon is an Equal Opportunity Employer. 
Epsilon is committed to promoting diversity, inclusion, and equal employment opportunities by using reasonable efforts to attract, recruit, engage and retain qualified individuals of all ethnicities and backgrounds, including, but not limited to, women, people of color, LGBTQ individuals, people with disabilities and any other underrepresented groups, traits or characteristics.