Fast Facts
Seeking a Senior Software Engineer to design and operate production-scale retrieval-augmented generation (RAG) systems in the healthcare domain, focusing on document retrieval and response generation.
Responsibilities: Key responsibilities include architecting and implementing end-to-end RAG workflows, building low-latency services and APIs, collaborating with cross-functional teams, and ensuring security and compliance standards.
Skills: Candidates must have strong programming skills in Python, experience with RAG systems, ML workflow tooling, cloud infrastructure, and a solid understanding of data governance and security best practices.
Qualifications: Preferred qualifications include familiarity with agentic workflow tools, knowledge of evaluation methodologies for retrieval systems, and experience with performance optimization for AI services.
Location: The job is based in Philadelphia, PA, with no specified travel requirements.
Compensation: $86600 - $144400 / Annually
Job title: Senior Software Engineer – Retrieval-Augmented Generation (RAG) System
About the role, 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.
About the team; This collaborative team is entrusted with building the Next Generation Health Solutions through the utilization of cutting-edge technology.
Role and responsibilities
- Architecting, implementing, testing, and operating end-to-end RAG workflows:
- Ingesting and normalizing documents from diverse sources
- Generating and managing embeddings; index and query vector databases
- Retrieve relevant passages, apply reranking or fusion strategies, and feed prompts to LLMs
- Building scalable, low-latency services and APIs (Python preferred; other languages acceptable) and ensure production-grade reliability (monitoring, tracing, alerting)
- Integrating with vector databases and embedding pipelines and optimize for latency, throughput, and cost
- Designing and implementing ML Ops workflows: model/version management, experiments, feature stores, CI/CD for ML-enabled services, rollback plans
- Developing robust data pipelines and governance around ingestion, provenance, quality checks, and access controls
- Collaborating 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)
- Implementing monitoring and observability for RAG components (latency, success rate, cache hit rate, retrieval quality, data drift)
- Ensuring security, privacy, and compliance (authentication, authorization, data masking, PII handling, audit logging)
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
Elsevier is a renowned global information analytics company that primarily focuses on providing scientific, technical, and medical (STM) research content, tools, and services. It is one of the largest publishers of academic journals and scholarly literature in the world.
Elsevier operates in various domains, including science, technology, medicine, social sciences, and more. They publish a vast number of peer-reviewed journals covering a wide range of disciplines. These journals act as platforms for researchers and academics to share their findings and contribute to the advancement of knowledge in their respective fields.
U.S. National Base Pay Range: $86,600 - $144,400. Geographic differentials may apply in some locations to better reflect local market rates. If performed in New Jersey, the base pay range is $97,867 - $156,333. This job is eligible for an annual incentive bonus.
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