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
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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

  • 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

  • Sentence Transformers

  • OpenAI Embeddings

  • Multilingual Embedding Models


Fine-Tuning & Model Training

Practical experience with:

  • LoRA

  • QLoRA

  • PEFT

  • Hugging Face Trainer

  • TRL

  • Unsloth

  • Axolotl

  • Dataset preparation

  • Synthetic data generation

  • Model benchmarking

  • Evaluation pipelines

  • Reinforcement Learning from Human Feedback (RLHF) - Preferred


AI Agents & Automation

Experience with:

  • MCP (Model Context Protocol)

  • CrewAI

  • LangGraph

  • AutoGen

  • OpenAI Agents SDK

  • n8n

  • AI Workflow Automation

  • Tool Calling

  • Memory Systems

  • Multi-Agent Collaboration


Backend Development

Strong experience with:

  • Python

  • FastAPI

  • REST APIs

  • WebSockets

  • Async Programming

  • Docker

  • Linux

Experience with PHP/Laravel or Node.js is an added advantage for enterprise integrations.


Cloud & Infrastructure

Experience deploying AI systems on:

  • AWS

  • GPU Servers

  • Linux

  • Docker

  • Kubernetes (preferred)

  • Nginx

  • Reverse Proxy

  • SSL

  • DNS

  • CI/CD Pipelines


Research & Evaluation

Candidates should be comfortable:

  • Comparing open-source models.

  • Benchmarking inference performance.

  • Testing new foundation models.

  • Reading AI research papers.

  • Evaluating model quality.

  • Measuring latency and throughput.

  • Selecting the right model for production workloads.


Mandatory Requirements

  • Minimum 5 years of professional software engineering experience.

  • Minimum 2+ years building production AI/LLM applications.

  • Demonstrable experience with RAG pipelines.

  • Experience deploying local open-source LLMs.

  • Strong Git workflow and collaborative development practices.

GitHub Portfolio (Mandatory)

Please include:

  • GitHub Profile URL

  • AI/LLM repositories

  • RAG implementations

  • Open-source contributions

  • Technical blog (if available)

  • Hugging Face profile (optional)

  • Research papers (optional)

Applications without a GitHub profile showcasing relevant AI work may not be considered.


Preferred Experience

  • Self-hosting enterprise AI infrastructure

  • AI copilots

  • PrivateGPT deployments

  • MCP Servers

  • AI coding assistants

  • Voice AI

  • Vision-Language Models (VLMs)

  • OCR pipelines

  • Edge AI

  • On-premise AI deployments

  • AI security and governance


Why Join Cyberei?

  • Build AI products using the latest open-source models.

  • Work with enterprise-scale AI deployments.

  • Experiment with new LLMs before they reach mainstream adoption.

  • Design privacy-first AI infrastructure.

  • Contribute to cutting-edge AI engineering and open-source innovation.


Application Checklist

Before applying, please ensure your application includes:

  • GitHub Profile URL (Mandatory)

  • Portfolio Website

  • LinkedIn Profile

  • Resume

  • AI project links

  • Live demos (if available)

  • Details of the LLMs you have worked with

  • RAG pipeline architecture you've built

  • Open-source contributions

  • 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:

  • Full Resume / CV

  • GitHub Profile URL (Mandatory) showcasing your AI/LLM work

  • Portfolio Website (if available)

  • LinkedIn Profile

  • Live AI project links or demos

  • Details of your RAG pipeline implementations

  • Open-source contributions

  • Models you've deployed or fine-tuned (Llama, DeepSeek, Qwen, Mistral, Gemma, etc.)

  • Experience with local model deployment (Ollama, vLLM, llama.cpp, Hugging Face, TGI, etc.)

  • Details of vector databases and embedding models used

  • 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.

Skill Required

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Job Detail

Location
Remote, Hybird, Inoffice
Company
Cyberei Technologies
Type
Full time
Positions
1
Quantity to be recruited
100
Seniority
...
degree
...
Apply Before
2029-07-31