AI Engineer at Virallens

Bengaluru, karnataka, India -

Full Time

Start Date

Immediate

Expiry Date

22 Jun, 26

Salary

0.0

Posted On

24 Mar, 26

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, RAG, TensorFlow, PyTorch, Vector Search, Knowledge Graphs, Embeddings, LLMs, Hybrid Search, Metadata Filtering, Milvus, pgvector, FAISS, Weaviate, LangChain, LlamaIndex

Industry

Advertising Services

Description

Role : AI Engineer Experience : 0 - 3 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: 0 - 3 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.

Responsibilities

The role involves designing, prototyping, and deploying scalable Retrieval-Augmented Generation (RAG) systems, including architecting pipelines that utilize vector search and hybrid retrieval techniques. Responsibilities also include building knowledge graphs, optimizing data pipelines for embeddings, and evaluating/fine-tuning various Large Language Models (LLMs).