Senior Software Developer - Retrieval-Augmented ...

Senior Software Engineer - Retrieval-Augmented Generation (RAG) SystemWe 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 responsibilitiesArchitect, implement, test, and operate end-to-end RAG workflows:Ingest and normalize documents from diverse sourcesGenerate and manage embeddings; index and query vector databasesRetrieve relevant passages, apply reranking or fusion strategies, and feed prompts to LLMsBuild 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 costDesign and implement ML Ops workflows: model/version management, experiments, feature stores, CI/CD for ML-enabled services, rollback plansDevelop robust data pipelines and governance around ingestion, provenance, quality checks, and access controlsCollaborate 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 engineersCollaborate with product, design, and data teams to translate requirements into robust software solutionsDocument APIs, runbooks, and architectural decisions; participate in code reviews and design reviewsRequired qualifications5+ years of professional software engineering experience designing and delivering production systemsStrong programming skills (Python required; NodeJs a plus)Deep understanding of retrieval-augmented or application-scale NLP systems and practical experience building RAG-like pipelinesHands-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 teamsFamiliarity with data governance, privacy, and security best practicesPreferred qualificationsExperience with agentic workflow tools (LangGraph) and familiarity with prompt engineering for LLMsExposure to working with and evaluating different LLMsKnowledge of evaluation methodologies for retrieval and QA systems and the ability to set up A/B tests and dashboardsExperience with data processing frameworks (SQL, Pandas, Spark) and working with large-scale data pipelinesBackground in performance optimization for low-latency AI services (MLflow)Experience with monitoring and logging via New Relic, K9s, Portkey, etcExperience with minimizing token usage and cost optimizationComfortable with design and implementation of security controls for data-intensive AI systems

Created: 2026-05-09