AI Engineer

Role : AI Engineer

Experience : 3 - 8 years

About the Company

Virallens is a forward-thinking, technology-driven organisation dedicated to helping businesses scale, innovate, and lead in the age of Artificial Intelligence. We specialize in building intelligent solutions powered by cutting-edge generative AI technologies, transforming ideas into impactful real-world applications across a wide range of industries.

At Virallens, we thrive on collaboration, creativity, and speed. Our fast-paced environment empowers innovators, problem-solvers, and visionaries to make an immediate impact while shaping the future of AI-driven transformation.

About the Role

Responsibilities

• Design, prototype & deploy retrieval-augmented generation systems: Architect scalable RAG pipelines that combine vector search, hybrid retrieval, re-ranking and contextual compression techniques. Build and integrate vector search systems (e.g. Milvus, pgvector, FAISS, Weaviate) for high-recall retrieval across structured and unstructured data.
• Develop hybrid retrieval and knowledge-driven pipelines: Design hybrid retrieval systems that blend semantic, symbolic and graph-based methods. Create custom chunking and encoding strategies to store operational knowledge in vector databases and knowledge graphs.
• Build knowledge graphs & integrate them into retrieval workflows: Architect knowledge graphs (Neo4j, RDF, custom schemas) and integrate them into retrieval workflows to support reasoning and decision-making.
• Optimise data pipelines and embeddings: Build and optimise data pipelines that convert incoming documents into high-quality embeddings for AI retrieval. Tune chunk sizes, indexing frequencies and embedding strategies to enhance recall, factual accuracy and efficiency.
• Implement hybrid search & metadata filtering: Combine semantic and keyword search to improve precision and efficiency. Experiment with metadata filtering techniques to surface the most relevant context for AI reasoning agents.
• Evaluate & monitor system performance: Evaluate end-to-end retrieval performance using classical IR metrics (precision, recall) and LLM-specific evaluations (factuality, coherence, task success). Monitor retrieval logs and adjust embedding configurations to maintain relevance and mitigate hallucinations.
• Compare & fine-tune LLMs: Compare performance of different LLMs (e.g. GPT-4, Claude, Llama) across embedding structures and refine tuning strategies. Implement quantisation, distillation and optimisation techniques to meet latency, throughput and cost targets.
• Collaborate & enable teams: Work cross-functionally with product managers, data engineers and domain experts to translate product goals into scalable AI solutions. Conduct workshops and enablement sessions to enhance AI literacy across internal teams.
• Ensure quality & compliance: Participate in rigorous code reviews and implement testing frameworks to ensure reliability, security and compliance. Continuously monitor model accuracy and safety, and uphold data governance and ethical guidelines.

Qualifications

• Experience: 3 - 8 years of software engineering experience with deep expertise in Python, experience building and deploying RAG or information-retrieval systems, and strong proficiency in TensorFlow and PyTorch.
• Retrieval expertise: Demonstrated ability to design hybrid retrieval pipelines, encode knowledge using LLMs and vector stores, and build and optimise RAG systems.
• Vector databases & search algorithms: Proficiency with vector databases and search libraries such as pgvector, FAISS, Milvus, Pinecone or Weaviate, and strong understanding of vector search algorithms, indexing strategies and hybrid search techniques.
• Embedding & LLM frameworks: Hands-on experience with embeddings and transformer-based models (e.g. OpenAI, Cohere, Sentence Transformers) and frameworks such as Hugging Face Transformers, LangChain and LlamaIndex.
• Distributed systems & deployment: Practical knowledge of distributed systems, ETL pipelines, Docker and Kubernetes, along with cloud platforms (Azure, AWS, GCP) for deploying AI applications.
• Evaluation & security: Familiarity with evaluation of retrieval systems, observability tools and model performance monitoring. Understanding of data governance, security and compliance considerations.

Preferred Skills

• Knowledge graphs & multimodal search: Experience designing and deploying knowledge graphs, semantic graphs or multi-modal search systems.
• Fine-tuning & RLHF: Familiarity with LLM fine-tuning, reinforcement learning from human feedback (RLHF) and safety alignment.
• Multimodal AI: Exposure to multimodal models (image, video, audio) and diffusion models.
• Open-source contributions: Contributions to open-source generative AI, retrieval or vector database projects, or published research/blogs.
• Front-end prototyping: Experience with React/Next.js for rapid prototyping of AI-driven applications.
• Advanced degrees: Master’s or PhD in Computer Science, AI, Machine Learning or related fields (preferred but not mandatory). Extensive relevant experience or significant open-source contributions may substitute formal education.