We are looking for an experienced AI / LLM Engineer to design, build, and maintain intelligent applications powered by Large Language Models (LLMs), embeddings, similarity search, and vector databases . The ideal candidate will work on building real-time AI systems such as chatbots, semantic search, recommendation systems, document intelligence, and autonomous AI workflows.
You will be responsible for the end-to-end lifecycle of AI pipelines that include data ingestion, embedding generation, vector storage, retrieval, and LLM-based response generation or automated actions.
Experience : 5–7 Years
Location : Bangalore
Employment Type : Full-Time
Key Responsibilities
- Design and implement embedding pipelines for text, documents, images, or structured data.
- Build and optimize semantic search and similarity search systems using vector databases.
- Integrate and manage vector databases such as :
- Pinecone, Weaviate, Milvus, FAISS, Chroma, OpenSearch Vector Engine, etc.
- Develop LLM-powered applications for :
- Chatbots
- Q&A systems
- Recommendation engines
- AI agents and automation workflows
- Implement RAG (Retrieval Augmented Generation) pipelines.
- Fine-tune prompt strategies and system prompts for optimal LLM performance.
- Integrate LLMs such as :
- OpenAI, Azure OpenAI, Anthropic, Google Gemini, Meta LLaMA, Mistral, etc.
- Build APIs and microservices for AI features using :
- Python / Java / Node.js / Spring Boot / FastAPI
- Implement similarity scoring, ranking, filtering, and metadata-based retrieval .
- Monitor, optimize, and scale vector search performance.
- Handle LLM cost optimization, latency reduction, and caching strategies .
- Implement AI safety, hallucination reduction, and response validation mechanisms .
- Work closely with product, frontend, and data teams.
- Deploy AI workloads on cloud platforms (AWS, Azure, GCP, OCI) .
- Maintain CI / CD pipelines for AI services.
Required Skills & Qualifications
Mandatory Core AI & LLM Skills
Strong understanding of :EmbeddingsVector similarity searchCosine similarity, dot product, ANN indexingHands-on experience with :LangChain / LlamaIndex / Semantic Kernel / Spring AIExperience with RAG architecturesExperience with at least one Vector DatabaseProficient in prompt engineering and LLM orchestrationProgramming & Backend
Strong in Python / Java / JavaScript / TypeScriptAPI development with FastAPI, Flask, Spring Boot, or Node.jsStrong understanding of REST APIs, background jobs, async processingData & Storage
Experience with :PostgreSQL, MySQL, MongoDBObject storage (S3, OCI, Azure Blob)Data preprocessing, chunking, tokenization strategiesCloud & DevOps (Good to Have)
Docker & KubernetesCI / CD pipelines (Jenkins, GitHub Actions, GitLab, Bitbucket)Monitoring with Prometheus, Grafana, OpenTelemetryGood to Have (Preferred Skills)
Experience with Agentic AI frameworksKnowledge of Tool Calling / Function CallingExperience with Speech-to-Text and Vision modelsFine-tuning or LoRA experienceKnowledge of security & data privacy for AI systemsExperience building autonomous AI workflowsUse Cases You Will Work On
AI chatbots for customer supportSemantic document searchKnowledge-base Q&A systemsRecommendation enginesAutomated ticket triagingAI assistants for developers or operationsIntelligent workflow automation