AI Retrieval & Relevance Engineer (RAG/Hybrid search)

Overview

About Nortal: 

We’re Nortal. We think big and create cutting-edge digital solutions with a global reach. And with 25 years of experience, 2,700+ professional experts, and half a billion people worldwide impacted by our work, we believe we’ve got the numbers to back up that statement.  

Our global teams have played a significant role in many Fortune 500 companies’ projects and systems and have been the driving force of digital transformation for governments, healthcare institutions, and leading enterprises worldwide. We combine best-in-class strategic consulting with software engineering, data, and design practices to bring our visions to life. 

The client you’ll work with: 

iBusiness is a leading technology company transforming the way financial institutions, small businesses, and enterprises work. As a pioneer in secure AI, automation, and AI software development, we build infrastructure and platforms that empower teams to modernize processes and work more efficiently without sacrificing compliance or security. Our workflow, verticalized, and point solutions enable seamless digital transformation, giving organizations of all sizes the tools they need to compete, innovate, and grow.

Join us and be part of a team that’s building solutions designed to help businesses thrive!

The role:

We are seeking an experienced AI Retrieval & Relevance Engineer to architect, implement, and optimize retrieval augmented generation (RAG) and hybrid search systems that provide accurate, grounded context to LLMs and AI agents. This role owns the retrieval pipeline end-to-end - from indexing strategy and candidate generation through ranking/reranking and evaluation to ensure our systems efficiently retrieve, contextualize, and support accurate outputs across business applications. You will collaborate closely with Knowledge Representation engineering to leverage knowledge graphs and semantic signals in retrieval.

Responsibilities

RAG Architecture & Hybrid Retrieval

• Architect, implement, and optimize RAG workflows integrating LLMs with retrieval mechanisms (vector search, Elasticsearch, FAISS, Weaviate).
• Implement and optimize dense/sparse/hybrid retrieval strategies, ranking algorithms, reranking, and query rewriting to maximize relevance and accuracy.
• Integrate graph-aware retrieval patterns (entity centric expansion, metadata filters, constrained traversal) using signals defined by Knowledge Representation.
• Indexing, Ingestion-to-Retrieval Pipelines (Retrieval View)
• Design and maintain scalable pipelines for indexing and retrieval readiness: chunking, embedding, metadata enrichment, index refresh and backfills.
• Ensure reliable retrieval across structured and unstructured data with appropriate filtering, boosting, and context packaging strategies.

Training Data Operations (Retrieval & Evals)

• Orchestrate and scale retrieval-related training/evaluation data operations, including: query sets / golden datasets, relevance judgments, regression suites and benchmarks lineage and versioning of eval datasets

Evaluation, Observability, and Performance

• Define and run retrieval evaluation: recall@k, nDCG/MRR, context precision, and groundedness/citation success metrics.
• Instrument telemetry and dashboards for retrieval quality, drift, latency (p95/p99), and cost.
• Optimize performance and reliability: caching, rate limiting, tiered retrieval, fallbacks.

Agent Tooling & Addressable Services

• Design and build addressable retrieval services/tools that can be invoked by LLMs and agents to orchestrate workflows (query endpoints, retrieval tools, context assembly services).

Collaboration & Documentation

• Work with Knowledge Representation engineering to align on entity/metadata contracts and provenance signals used in retrieval.
• Maintain clear documentation of retrieval models, pipelines, evals, and runbooks.
• Evaluate and integrate new technologies and research in information retrieval, RAG, and vector search.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or related field (or equivalent experience)
• Proven experience designing and tuning information retrieval systems, vector search, and RAG frameworks
• Strong knowledge of vector and hybrid search technologies (e.g., FAISS, Weaviate, Elasticsearch, Milvus/Pinecone equivalents)
• Proficiency in Python and familiarity with ML tooling (PyTorch/TensorFlow helpful, especially for rerankers)
• Familiarity with distributed processing/orchestration tools (e.g., Spark, Airflow, Kubeflow) as needed for indexing and eval pipelines
• Strong analytical and communication skills; able to collaborate cross-functionally

Brownie points:

• Experience with rerankers / learning-to-rank, query understanding, and relevance tuning.
• Experience with LLM fine-tuning, prompt engineering, and RAG optimization.
• Familiarity with agentic systems and multi-step retrieval (iterative retrieval, tool-use patterns).
• Cloud and scalable storage/indexing platform experience

Why work with us:

• Great work-life balance
• Competitive remuneration package
• Exceptional social package & special discounts
• Supplemental health & dental care
• Team bonding events
• Excellent office location & facilities
• Relaxing & gaming areas
• Free bike parking & showers

How to apply:

If you recognize yourself in this role, please send your CV in English.

Our team would be excited to get in touch if your skillset and experience match the profile we look for.

Of course, all applications will be treated as strictly confidential.