Senior AI Engineer
Duration: 2026-07-23 - 2029-07-31
Monthly Salary: ₹0.00 - ₹0.00
Job position: Software Development Engineer
Cyberei Technologies
Prestige Atlanta, 80 Feet Rd, Koramangala 1A Block, Koramangala 3 Block, Koramangala, Bengaluru, Karnataka 560034
Job description
Senior AI Engineer - LLM, RAG & Local AI Systems (5+ Years)
About Cyberei Technologies
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 don't 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, we'd 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 You'll Build
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Enterprise AI Assistants
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AI Agents & Multi-Agent Systems
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Retrieval-Augmented Generation (RAG) Platforms
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Local LLM Inference Servers
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Knowledge Base Search Engines
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AI Document Intelligence Systems
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AI Workflow Automation
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Self-Hosted Enterprise AI Platforms
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AI APIs & Model Gateways
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Private GPT Solutions
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AI SaaS Products
Core Responsibilities
As a Senior AI Engineer, you will:
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Design, build, and deploy enterprise-grade AI applications using open-source LLMs.
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Build scalable Retrieval-Augmented Generation (RAG) pipelines from scratch.
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Design document ingestion pipelines supporting PDFs, Office documents, websites, databases, APIs, images, and structured enterprise data.
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Implement semantic search using embedding models and vector databases.
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Deploy and optimize local AI models for production environments.
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Evaluate, benchmark, fine-tune, and optimize open-source LLMs.
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Build AI agent workflows using modern orchestration frameworks.
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Develop production-ready inference APIs.
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Optimize GPU and CPU inference performance.
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Work with quantized models (GGUF, AWQ, GPTQ, FP16, INT4, INT8).
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Build AI-powered search, recommendation, summarization, translation, OCR, and conversational systems.
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Integrate AI models with existing enterprise applications.
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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:
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Llama
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DeepSeek
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Qwen
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Mistral
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Gemma
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Phi
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Mixtral
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Falcon
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Granite
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OpenChat
Must understand:
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Context windows
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Prompt engineering
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Structured outputs
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Function calling
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Tool use
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Model evaluation
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Hallucination mitigation
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Guardrails
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Reasoning models
Local AI Deployment
Strong experience with:
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Ollama
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vLLM
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LM Studio
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llama.cpp
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Hugging Face Transformers
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Text Generation Inference (TGI)
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NVIDIA CUDA
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ROCm (preferred)
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GPU optimization
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Quantization techniques
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Multi-model serving
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Self-hosted inference infrastructure
Retrieval-Augmented Generation (RAG)
Hands-on production experience with:
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LangChain
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LlamaIndex
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Haystack
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DSPy (preferred)
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Agentic RAG
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Hybrid Search
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Parent-Child Retrieval
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Multi-Query Retrieval
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Context Compression
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Metadata Filtering
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Re-ranking
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Embedding Optimization
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Citation Generation
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Knowledge Graph Integration
Vector Databases
Experience with one or more:
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Qdrant
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ChromaDB
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FAISS
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Milvus
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Pinecone
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Weaviate
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pgvector
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Elasticsearch
Embedding Models
Experience with:
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BAAI BGE
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E5
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Jina Embeddings
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Nomic Embeddings
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Sentence Transformers
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OpenAI Embeddings
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Multilingual Embedding Models
Fine-Tuning & Model Training
Practical experience with:
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LoRA
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QLoRA
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PEFT
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Hugging Face Trainer
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TRL
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Unsloth
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Axolotl
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Dataset preparation
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Synthetic data generation
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Model benchmarking
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Evaluation pipelines
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Reinforcement Learning from Human Feedback (RLHF) - Preferred
AI Agents & Automation
Experience with:
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MCP (Model Context Protocol)
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CrewAI
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LangGraph
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AutoGen
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OpenAI Agents SDK
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n8n
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AI Workflow Automation
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Tool Calling
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Memory Systems
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Multi-Agent Collaboration
Backend Development
Strong experience with:
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Python
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FastAPI
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REST APIs
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WebSockets
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Async Programming
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Docker
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Linux
Experience with PHP/Laravel or Node.js is an added advantage for enterprise integrations.
Cloud & Infrastructure
Experience deploying AI systems on:
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AWS
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GPU Servers
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Linux
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Docker
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Kubernetes (preferred)
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Nginx
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Reverse Proxy
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SSL
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DNS
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CI/CD Pipelines
Research & Evaluation
Candidates should be comfortable:
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Comparing open-source models.
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Benchmarking inference performance.
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Testing new foundation models.
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Reading AI research papers.
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Evaluating model quality.
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Measuring latency and throughput.
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Selecting the right model for production workloads.
Mandatory Requirements
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Minimum 5 years of professional software engineering experience.
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Minimum 2+ years building production AI/LLM applications.
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Demonstrable experience with RAG pipelines.
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Experience deploying local open-source LLMs.
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Strong Git workflow and collaborative development practices.
GitHub Portfolio (Mandatory)
Please include:
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GitHub Profile URL
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AI/LLM repositories
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RAG implementations
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Open-source contributions
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Technical blog (if available)
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Hugging Face profile (optional)
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Research papers (optional)
Applications without a GitHub profile showcasing relevant AI work may not be considered.
Preferred Experience
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Self-hosting enterprise AI infrastructure
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AI copilots
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PrivateGPT deployments
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MCP Servers
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AI coding assistants
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Voice AI
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Vision-Language Models (VLMs)
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OCR pipelines
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Edge AI
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On-premise AI deployments
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AI security and governance
Why Join Cyberei?
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Build AI products using the latest open-source models.
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Work with enterprise-scale AI deployments.
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Experiment with new LLMs before they reach mainstream adoption.
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Design privacy-first AI infrastructure.
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Contribute to cutting-edge AI engineering and open-source innovation.
Application Checklist
Before applying, please ensure your application includes:
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GitHub Profile URL (Mandatory)
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Portfolio Website
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LinkedIn Profile
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Resume
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AI project links
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Live demos (if available)
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Details of the LLMs you have worked with
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RAG pipeline architecture you've built
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Open-source contributions
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Hugging Face profile/Token utliziation profiles (optional)
Application Instructions (Please Read Carefully)
To help us evaluate your application efficiently, please complete every field in the application form.
Applications with incomplete information are difficult to assess and may be rejected without further review.
Mandatory Information
Please ensure you provide:
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Full Resume / CV
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GitHub Profile URL (Mandatory) showcasing your AI/LLM work
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Portfolio Website (if available)
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LinkedIn Profile
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Live AI project links or demos
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Details of your RAG pipeline implementations
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Open-source contributions
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Models you've deployed or fine-tuned (Llama, DeepSeek, Qwen, Mistral, Gemma, etc.)
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Experience with local model deployment (Ollama, vLLM, llama.cpp, Hugging Face, TGI, etc.)
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Details of vector databases and embedding models used
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Any research papers, blogs, or technical articles (optional but appreciated)
Please Do Not Leave Any Field Blank
A complete application gives us a much better understanding of your experience and significantly increases the chances of your profile being shortlisted.
Candidates with well-documented projects, clean GitHub repositories, and production AI implementations will receive priority during the screening process.
Bonus Brownie Points ⭐
If you enjoy our hiring process and would like to support our engineering team, we'd truly appreciate a genuine Google review sharing what you liked most about your application experience.
Your feedback helps us continuously improve our hiring process and boosts the morale of our team.
Leave your review here:
Cyberei Technologies Google Review
Thank you for taking the time to apply.
We look forward to reviewing your work and building the future of AI together at Cyberei Technologies.