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
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