AI/ML Engineer – RAG

Company Overview

Viamagus is a forward-thinking technology company focused on building intelligent systems and solutions that drive business growth. Our teams work at the intersection of innovation and engineering, solving complex problems with cutting-edge AI/ML technologies. At Viamagus, we invest in our people and foster a culture of collaboration, curiosity, and continuous improvement.

Position Overview

Viamagus is hiring for an AI/ML Engineer – RAG. We are seeking a hands-on engineer specializing in Retrieval-Augmented Generation (RAG) to design, build, and optimize production-grade systems that ground LLM responses in enterprise knowledge. You will own end-to-end retrieval pipelines — from ingestion and indexing to hybrid search, reranking, and evaluation — ensuring high relevance, low latency, and measurable reductions in hallucinations and answer failures.

Key Responsibilities

RAG Pipeline Design & Production Deployment

• Design and implement robust RAG pipelines: ingestion, parsing, chunking, enrichment, embedding, indexing, retrieval, reranking, and answer generation.
• Choose and tune retrieval strategies (dense, sparse/lexical, and hybrid) to maximize recall and precision for real enterprise queries.
• Build citation/grounding mechanisms and response policies to ensure traceable, trustworthy outputs.

Indexing, Search Quality & Ranking

• Implement and optimize vector and hybrid search over structured and unstructured data (documents, wikis, tickets, logs, and metadata).
• Develop reranking strategies (cross-encoder, late-interaction, or LLM-based) and fusion methods (RRF/weighted fusion) to improve ranking quality.
• Establish query understanding and rewriting techniques (intent classification, expansions, entity/keyword boosting) to improve retrieval robustness.

Evaluation, Guardrails & Continuous Improvement

• Define an evaluation harness for retrieval and generation using offline datasets and online telemetry (precision/recall@k, MRR/nDCG, groundedness).
• Implement automated regression tests and quality gates for new prompts, retrievers, and model updates.
• Create feedback loops using human review and lightweight labeling to improve relevance over time.

Performance, Reliability & Cost Efficiency

• Optimize latency and throughput using caching, batching, streaming responses, and efficient retrieval/index configurations.
• Instrument the full pipeline with logs, metrics, traces, dashboards, and alerting; triage failures with runbooks.
• Drive cost-aware design across embedding, retrieval, and generation (token budgets, context windows, adaptive retrieval).

Security, Access Control & Compliance

• Implement document-level security and access control in retrieval (ACL-aware indexing, filtering, or query-time authorization checks).
• Ensure safe handling of sensitive data, auditability, and compliance with enterprise governance standards.

Collaboration & Enablement

• Partner with domain owners and engineering teams to prioritize use cases and integrate RAG into products and workflows.
• Document best practices and provide reusable templates for ingestion, evaluation, and deployment.

Required Qualifications

• Bachelor's degree in computer science, Engineering, Data Science, Human-Computer Interaction, or a related field with 5+ years of relevant experience; OR a Master's/PhD with 3+ years of relevant experience.
• Strong programming skills in Python and experience with LLM/RAG development in production environments.
• Experience with vector databases or search engines and retrieval concepts (ANN indexes, BM25/lexical search, hybrid retrieval).
• Experience designing evaluation methods for retrieval and LLM outputs (grounding, relevance, factuality, and regression testing).
• Experience building scalable services and APIs (REST/gRPC), with attention to reliability and performance.
• Strong understanding of data processing pipelines, metadata design, and information retrieval fundamentals.
• Excellent communication skills and ability to work effectively in cross-functional teams.

Preferred Qualifications

• Experience with ranking/reranking techniques (cross-encoders, late-interaction, learning-to-rank) and fusion methods (RRF, weighted scoring).
• Experience with document parsing for PDFs/HTML and handling tables, diagrams, or mixed layouts.
• Experience with observability and SRE practices for AI systems (SLOs/SLIs, incident response, runbooks).
• Experience implementing ACL-aware retrieval and security patterns for enterprise knowledge systems.
• Experience building prompt/tooling libraries and maintaining multi-model compatibility across LLM providers.