Sr AI Engineer

Job highlights

• Reasoning models. Local AI Deployment . Strong experience with: . Ollama. vLLM. LM Studio. llama.cpp. Hugging Face Transformers.
• Experience: Minimum 5 Years (Mandatory). Employment Type: Full-Time. Department: Artificial Intelligence Research Engineering
• Must understand: .
• Experience with: . BAAI BGE. E5. Jina Embeddings. Nomic Embeddings

Job Summary

Senior AI Engineer - LLM, RAG Local AI Systems (5+ Years)

At Cyberei Technologies, we are building AI-native products powered by open-source Large Language Models. Our mission is to reduce AI infrastructure costs by running production-grade LLMs locally while delivering enterprise AI solutions with complete data privacy, ownership, and scalability.

We are looking for engineers who dont just consume AI APIs but build, optimize, deploy, fine-tune, and productionize open-source AI models.

If your expertise lies in LLMs, RAG pipelines, AI agents, local inference, model evaluation, and enterprise AI architecture, wed love to work with you.

Position Senior AI Engineer - LLM, RAG Local AI Systems

Experience: Minimum 5 Years (Mandatory)

Employment Type: Full-Time

Department: Artificial Intelligence Research Engineering

What Youll Build

• Enterprise AI Assistants
• AI Agents Multi-Agent Systems
• Retrieval-Augmented Generation (RAG) Platforms
• Local LLM Inference Servers
• Knowledge Base Search Engines
• AI Document Intelligence Systems
• AI Workflow Automation
• Self-Hosted Enterprise AI Platforms
• AI APIs Model Gateways
• Private GPT Solutions
• AI SaaS Products

Core Responsibilities

As a Senior AI Engineer, you will:

• Design, build, and deploy enterprise-grade AI applications using open-source LLMs.
• Build scalable Retrieval-Augmented Generation (RAG) pipelines from scratch.
• Design document ingestion pipelines supporting PDFs, Office documents, websites, databases, APIs, images, and structured enterprise data.
• Implement semantic search using embedding models and vector databases.
• Deploy and optimize local AI models for production environments.
• Evaluate, benchmark, fine-tune, and optimize open-source LLMs.
• Build AI agent workflows using modern orchestration frameworks.
• Develop production-ready inference APIs.
• Optimize GPU and CPU inference performance.
• Work with quantized models (GGUF, AWQ, GPTQ, FP16, INT4, INT8).
• Build AI-powered search, recommendation, summarization, translation, OCR, and conversational systems.
• Integrate AI models with existing enterprise applications.
• Continuously evaluate emerging open-source AI models and recommend the best fit for production use.

Required Technical Expertise

Large Language Models

Hands-on experience with multiple open-source models, including but not limited to:

• Llama
• DeepSeek
• Qwen
• Mistral
• Gemma
• Phi
• Mixtral
• Falcon
• Granite
• OpenChat

Must understand:

• Context windows
• Prompt engineering
• Structured outputs
• Function calling
• Tool use
• Model evaluation
• Hallucination mitigation
• Guardrails
• Reasoning models

Local AI Deployment

Strong experience with:

• Ollama
• vLLM
• LM Studio
• llama.cpp
• Hugging Face Transformers
• Text Generation Inference (TGI)
• Nvidia CUDA
• ROCm (preferred)
• GPU optimization
• Quantization techniques
• Multi-model serving
• Self-hosted inference infrastructure

Retrieval-Augmented Generation (RAG)

Hands-on production experience with:

• LangChain
• LlamaIndex
• Haystack
• DSPy (preferred)
• Agentic RAG
• Hybrid Search
• Parent-Child Retrieval
• Multi-Query Retrieval
• Context Compression
• Metadata Filtering
• Re-ranking
• Embedding Optimization
• Citation Generation
• Knowledge Graph Integration

Vector Databases

Experience with one or more:

• Qdrant
• ChromaDB
• FAISS
• Milvus
• Pinecone
• Weaviate
• pgvector
• Elasticsearch

Embedding Models

Experience with:

• BAAI BGE
• E5
• Jina Embeddings
• Nomic Embeddings

Disclaimer: This job posting and Location has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.