For our client, we are looking for a Senior AI/ML Engineerto join an AI project and drive the design, development, and optimization of cutting-edge retrieval-augmented generation (RAG) solutions.
About the Role
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.
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