Senior Artificial Intelligence/Machine Learning Engineer

Ciklum is looking for a Senior Artificial Intelligence/Machine Learning Engineer to join our team full-time in the Czech Republic.

We are a custom product engineering company that supports both multinational organizations and scaling startups to solve their most complex business challenges. With a global team of over 4,000 highly skilled developers, consultants, analysts and product owners, we engineer technology that redefines industries and shapes the way people live.

About the role:

As a Senior Artificial Intelligence/Machine Learning Engineer, become a part of a cross-functional development team to take technical ownership of the RAG core and vector store on a complex, enterprise-scale AI project. In this role, you will lead the Phase 0 Vector DB Proof of Concept, design and own the full retrieval pipeline, and set the RAG evaluation approach against a real production corpus. This is a high-impact position for an engineer who combines deep RAG expertise with strong engineering discipline and a passion for building AI systems that perform reliably at scale.

About the project:

You will join a team building an enterprise-scale AI Knowledge Management platform for a large European software company in the healthcare IT space. The mission is to deploy an advanced Retrieval-Augmented Generation (RAG) system that automates, structures, and synchronises critical support ticket data. The platform extends existing infrastructure including a PostgreSQL proof-of-concept at 2M documents and an SAP AI Core configuration rather than rebuilding from scratch. The core challenges are confirming the
final vector DB and embedding stack, designing PII-safe multilingual ingestion, and ensuring
bidirectional sync with NICE CXone as the production knowledge base, all within EU data
residency and GxP/GDPR constraints.

Responsibilities: 

• Serve as technical lead for the RAG core and vector store throughout the project lifecycle
• Lead the Phase 0 Vector DB Proof of Concept evaluating, testing, and confirming the final stack before build begins
• Design and own the hybrid retrieval pipeline, including HNSW/BM25 retrieval strategies and tuning
• Build and maintain the multilingual embedding pipeline, selecting and validating appropriate embedding models for the target corpus
• Design and implement the LLM router, confidence scoring logic, and feedback loop to ensure reliable and accurate RAG outputs
• Define and own the RAG evaluation approach against the client’s real KB and support ticket corpus
• Own RAG quality end to end across all pilot PCUs, monitoring and iterating on retrieval and generation performance
• Collaborate closely with the Solution Architect on infrastructure design and integration decisions
• Work with the BA team to ensure technical solutions align with content taxonomy, schema definitions, and workflow requirements
• Apply MLOps best practices for pipeline versioning, monitoring, and continuous improvement.
• Ensure all AI components comply with GxP, GDPR, and EU data residency requirements

Requirements: 

We know that sometimes, you can’t tick every box. We would still love to hear from you if you think you’re a good fit!

• 6+ years of Software/AI Engineering experience, including 3+ years specifically implementing AI/ML solutions
• Hands-on expertise architecting high-performance RAG and hybrid search systems specifically optimizing chunking strategies, embedding selection/evaluation, vector databases, and retrieval strategies
• Proven experience building generative AI architectures, agentic systems, and Large Language Models (LLMs)
• Strong proficiency in Python with the ability to integrate models seamlessly into various backend environments
• Experience managing end-to-end AI lifecycles, orchestration, and evaluation tooling using robust MLOps practices (CI/CD, pipeline versioning, monitoring, deployment automation) on cloud platforms like AWS SageMaker, Azure AI, or GCP
• Familiarity with structured AI delivery (CRISP-ML(Q), TDSP), handling complex data (time-series, anomaly detection), and data governance/compliance standards
  (Security, GDPR; GxP exposure is a plus)
• Understanding of probability, statistics, and ML optimization
• Strong written and verbal English communication skills

What’s in it for you?

• Flexible working hours
• Home office option
• 5 weeks of holiday
• 5 sick days
• Multisport Card
• Meal allowance
• Internal trainings including workshops and seminars
• Paid certifications and technical as well as soft-skills training
• Possibility to participate in international conferences
• Fresh fruit, coffee, and