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For our client, we are looking for a Senior AI/ML Engineer to join an AI project and drive the design, development, and optimization of cutting-edge retrieval-augmented generation (RAG) solutions.
This role is ideal for a highly skilled engineer passionate about AI/ML systems, distributed architectures, and vector search technologies . You will play a key role in designing scalable inference stacks, optimizing retrieval pipelines, and integrating modern frameworks into production-grade applications.
Requirements
5+ years of experience in software engineering , preferably with AI/ML or distributed systems.
Proven hands-on experience with retrieval-augmented generation (RAG) systems .
Deep knowledge of OpenSearch, Elasticsearch or similar search engines.
Strong coding skills in Python .
Experience with LlamaIndex, LangChain .
Familiarity with vector databases (Pinecone, Qdrant, FAISS).
Exposure to LLM fine-tuning, embeddings, semantic search, prompt engineering .
Background in high-scale systems handling millions of users/queries daily.
Knowledge of cloud infrastructure (AWS/GCP/Azure) and containerization (Docker, Kubernetes).
Experience with vector search, embedding pipelines, dense retrieval techniques .
Strong optimization skills for latency, reliability, and scalability.
Excellent problem-solving, analytical, and debugging skills.
Proactive self-starter with ownership mindset.
Passion for impactful technology aligned with Geniusees mission.
Bachelors degree in Computer Science or equivalent practical experience.
English: Upper-Intermediate+ .
Candidates: Ukrainians (in Ukraine or abroad).
Responsibilities
Design, build, and scale production-grade inference stacks for RAG-based applications.
Develop efficient retrieval pipelines with OpenSearch/vector DBs ensuring high recall & relevance.
Optimize performance and latency for real-time and batch queries.
Identify and fix bottlenecks to improve system efficiency and response times .
Ensure observability, monitoring, and reliability of deployed systems.
Collaborate with teams to integrate LLMs and retrieval components into applications.
Evaluate and integrate modern RAG frameworks and tools .
Mentor team members, support architectural decisions , and uphold engineering excellence.
Contribute to pre-sales activities (NFR elicitation, solution architecture, risk definition).
Conduct discovery phases and recommend tools/libraries.
Lead/support code reviews , POCs, and R&D.
Interview external candidates and provide ad hoc troubleshooting.