{"id":744361,"url":"https://alion.io/job/virallens-ai-engineer","title":"AI Engineer","company":{"id":689480,"name":"Virallens","domain":"virallens.com","url":"https://alion.io/company/virallens","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Claude","optional":false},{"name":"Cohere SDK","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"ETL/ELT","optional":false},{"name":"FAISS","optional":false},{"name":"GCP","optional":false},{"name":"GPT-4","optional":false},{"name":"Hugging Face","optional":false},{"name":"Hybrid Search","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"Llama","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"Milvus","optional":false},{"name":"Model Distillation","optional":false},{"name":"Neo4j","optional":false},{"name":"OpenAI","optional":false},{"name":"pgvector","optional":false},{"name":"Pinecone","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Sentence-Transformers","optional":false},{"name":"TensorFlow","optional":false},{"name":"Transformers","optional":false},{"name":"Weaviate","optional":false},{"name":"Diffusion Models","optional":true},{"name":"Fine-tuning","optional":true},{"name":"JavaScript","optional":true},{"name":"Machine Learning","optional":true},{"name":"Multimodal AI","optional":true},{"name":"Next.js","optional":true},{"name":"PostgreSQL","optional":true},{"name":"React.js","optional":true},{"name":"Reinforcement Learning","optional":true},{"name":"RLHF","optional":true}],"status":"live","first_seen_at":"2026-07-16T00:00:00Z","employer_posted_date":"2026-07-16","last_verified_at":"2026-11-01T00:35:41Z","board_verified":true,"closed_at":null,"days_open":108,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":108},"description":"Role : AI Engineer\nExperience : 3 - 8 years\nAbout the Company\nVirallens 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.\nAt 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.\nAbout the Role\nResponsibilities\nDesign, 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.\nDevelop 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.\nBuild 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.\nOptimise 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.\nImplement 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.\nEvaluate & 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.\nCompare & 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.\nCollaborate & 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.\nEnsure 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.\nQualifications\nExperience: 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.\nRetrieval expertise: Demonstrated ability to design hybrid retrieval pipelines, encode knowledge using LLMs and vector stores, and build and optimise RAG systems.\nVector 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.\nEmbedding & 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.\nDistributed systems & deployment: Practical knowledge of distributed systems, ETL pipelines, Docker and Kubernetes, along with cloud platforms (Azure, AWS, GCP) for deploying AI applications.\nEvaluation & security: Familiarity with evaluation of retrieval systems, observability tools and model performance monitoring. Understanding of data governance, security and compliance considerations.\nPreferred Skills\nKnowledge graphs & multimodal search: Experience designing and deploying knowledge graphs, semantic graphs or multi-modal search systems.\nFine-tuning & RLHF: Familiarity with LLM fine-tuning, reinforcement learning from human feedback (RLHF) and safety alignment.\nMultimodal AI: Exposure to multimodal models (image, video, audio) and diffusion models.\nOpen-source contributions: Contributions to open-source generative AI, retrieval or vector database projects, or published research/blogs.\nFront-end prototyping: Experience with React/Next.js for rapid prototyping of AI-driven applications.\nAdvanced degrees: Master’s or PhD in Computer Science, AI, Machine Learning or related fields (preferred but not mandatory). 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