Retrieval-Augmented Generation (RAG) System - Senior Software Developer

Elsevier; Philadelphia (Philadelphia County); On-site; USD 120,000 - 150,000; Full time

Job summary

A leading health technology company is looking for a Senior Software Engineer specializing in Retrieval-Augmented Generation (RAG) systems. You will design and operate RAG pipelines, ensuring scalability and performance. Applicants should have at least 5 years of experience in software engineering, proficiency in Python, and a solid understanding of NLP systems. The position encourages collaboration with cross-functional teams and offers a dynamic environment focused on healthcare solutions.

Qualifications

• 5+ years of professional software engineering experience designing and delivering production systems.
• Deep understanding of retrieval‑augmented or application‑scale NLP systems.
• Hands‑on experience with ML workflow tooling and MLOps concepts.

Responsibilities

• Architect, implement, test, and operate end-to-end RAG workflows.
• Build scalable, low-latency services and APIs, ensuring production-grade reliability.
• Collaborate with product, design, and data teams to translate requirements into robust software solutions.

Skills

Software engineering

Python programming

NLP systems

Cloud infrastructure

Cross-functional teamwork

Tools

Docker

Kubernetes

AWS

GCP

Azure

Job description

Senior Software Engineer – Retrieval-Augmented Generation (RAG) System

We are seeking an engineer to work with a team to build and support a healthcare centered production-scale RAG system that combines document retrieval with response generation to deliver accurate, context-aware answers. This engineer we be expected to design, implement, and operate end-to‑end RAG pipelines— LLM interaction, API creation, and high‑performance, secure delivery of knowledge‑grounded capabilities. You will collaborate with data engineers, platform teams, and product partners to ship reliable, scalable, and observable systems.

Role and responsibilities

• Architect, implement, test, and operate end-to‑end RAG workflows:
• Ingest and normalize documents from diverse sources
• Generate and manage embeddings; index and query vector databases
• Retrieve relevant passages, apply reranking or fusion strategies, and feed prompts to LLMs
• Build scalable, low‑latency services and APIs (Python preferred; other languages acceptable) and ensure production‑grade reliability (monitoring, tracing, alerting)
• Integrate with vector databases and embedding pipelines and optimize for latency, throughput, and cost
• Design and implement ML Ops workflows: model/version management, experiments, feature stores, CI/CD for ML-enabled services, rollback plans
• Develop robust data pipelines and governance around ingestion, provenance, quality checks, and access controls
• Collaborate with data engineers to improve retrieval quality (embedding strategies, reranking, cross‑encoder models, prompt engineering) and implement evaluation metrics (precision/recall, MRR, QA accuracy, user‑centric metrics)
• Implement monitoring and observability for RAG components (latency, success rate, cache hit rate, retrieval quality, data drift)Ensure security, privacy, and compliance (authentication, authorization, data masking, PII handling, audit logging)
• Optimize for scalability and reliability in cloud environments (AWS/GCP/Azure) and containerized deployments (Docker, Kubernetes)
• Contribute to architecture decisions, drive technical debt reduction, and mentor junior engineers
• Collaborate with product, design, and data teams to translate requirements into robust software solutions
• Document APIs, runbooks, and architectural decisions; participate in code reviews and design reviews

Required qualifications

• 5+ years of professional software engineering experience designing and delivering production systems
• Strong programming skills (Python required; NodeJs a plus)
• Deep understanding of retrieval‑augmented or application‑scale NLP systems and practical experience building RAG‑like pipelines
• Hands‑on experience with ML workflow tooling and MLOps concepts (model serving, versioning, experiments, feature stores, reproducibility)
• Proficiency with cloud infrastructure and modern software practices (AWS/GCP/Azure; Docker; Kubernetes; CI/CD)
• Strong problem‑solving skills, excellent communication, and ability to work with cross‑functional teams
• Familiarity with data governance, privacy, and security best practices

Preferred qualifications

• Experience with agentic workflow tools (LangGraph) and familiarity with prompt engineering for LLMs
• Exposure to working with and evaluating different LLMs
• Knowledge of evaluation methodologies for retrieval and QA systems and the ability to set up A/B tests and dashboards
• Experience with data processing frameworks (SQL, Pandas, Spark) and working with large‑scale data pipelines
• Background in performance optimization for low‑latency AI services (MLflow)
• Experience with monitoring and logging via New Relic, K9s, Portkey, etc
• Experience with minimizing token usage and cost optimization
• Comfortable with design and implementation of security controls for data‑intensive AI systems