Senior AI Engineer

Cplusoft; Doha; On-site; QAR 400,000 - 800,000; Full time

Job summary

Cplusoft seeks a seasoned Senior AI Engineer to lead architecture, engineering, and deployment of production AI systems, bridging cutting-edge research with scalable cloud software. You will architect resilient pipelines, optimize low-latency inference, and drive multi-agent and RAG initiatives.

You will mentor engineers, establish MLOps best practices, and work with executive leadership to transform business challenges into production‑ready AI solutions in Doha.

Qualifications

• Experience building production AI applications and multi-agent systems.
• Strong background in data processing and embedding pipelines.
• Proven track record deploying scalable AI systems in cloud environments.

Responsibilities

• Lead architecture, engineering, and deployment of production AI systems.
• Mentor engineers and establish MLOps practices.
• Collaborate with leadership to translate business challenges into AI solutions.

Skills

Python expert

Rust/C++

LLMs & frameworks

MLOps

Cloud services

Microservices

RAG architectures

Vector databases

Performance optimization

REST/gRPC APIs

Tools

Docker

Kubernetes

FastAPI

TensorRT

ONNX Runtime

PyTorch

LangChain

Weaviate

Job description

We are seeking a seasoned and hands‑on Senior AI Engineer to lead the architecture, engineering, and deployment of production‑grade AI systems and intelligent workflows. In this role, you will bridge state‑of‑the‑art AI research with scalable cloud software engineering. You will lead technical direction, design resilient agentic systems and Retrieval‑Augmented Generation (RAG) architectures, fine‑tune models, and optimize low‑latency inference pipelines. As a senior engineering leader, you will mentor team members, establish MLOps best practices, and work directly with executive leadership to transform complex business challenges into production‑ready AI solutions.

Desired Candidate Profile

• Architect, build, and deploy end‑to‑end production AI applications, multi‑agent systems, and RAG architectures using modern frameworks (LangChain, LlamaIndex, AutoGen, CrewAI).

• Design, implement, and maintain high‑throughput data processing and embedding pipelines, integrating vector databases and hybrid search mechanisms.

• Fine‑tune open‑weight models (LLaMA, Mistral, Qwen) using techniques like LoRA, QLoRA, and DPO/RLHF, optimizing model inference via quantization, vLLM, TensorRT‑LLM, and ONNX Runtime.

• Build resilient microservices and low‑latency REST/gRPC APIs using Python (FastAPI), containerizing models with Docker and orchestrating workloads on cloud infrastructure.

• Implement production MLOps practices, including automated retraining, model versioning, prompt tracking, and experiment monitoring (MLflow, Weights & Biases, Arize).

• Establish observability, guardrails, and evaluation frameworks (Ragas, TruLens, Guardrails AI, NeMo) to track model drift, hallucination rates, latency, cost, and output quality.

• Drive engineering standards through code reviews, technical design documents, system architecture planning, and mentoring junior/mid‑level engineers.

• Stay current with fast‑evolving AI literature, open‑source model releases, and emerging frameworks to continually enhance application performance and velocity.

Required Technical Skills

• Core Languages & Systems: Python (expert level), Rust or C++ (strong plus), Data Structures, Systems Architecture, Concurrent Programming

• AI, LLMs & Frameworks: PyTorch, Hugging Face Transformers, LangChain, LlamaIndex, vLLM, DeepSpeed, Ollama

• Fine‑Tuning & Optimization: LoRA/QLoRA, PEFT, Unsloth, TensorRT‑LLM, Quantization (GGUF, AWQ, GPTQ)

• Vector Databases & Search: Qdrant, Pinecone, Milvus, Weaviate, FAISS, Elasticsearch/OpenSearch (Hybrid Search & Reranking)

• API & Microservices: FastAPI, gRPC, RESTful APIs, Docker, Kubernetes, Ray

• MLOps & Evaluation: MLflow, Weights & Biases, Phoenix/Arize, Ragas, LangSmith, DVC, GitHub Actions

• Cloud & Infrastructure: AWS (SageMaker, Bedrock, EC2, EKS), GCP (Vertex AI), or Azure AI, GPU orchestration (NVIDIA CUDA)

  
  

Preferred Qualifications

• Proven track record of taking complex LLM applications or multi‑agent systems from concept to high‑scale production serving real customer traffic.

• Experience with function calling, tool‑use agents, and structured outputs in mission‑critical applications.

• Familiarity with privacy‑preserving AI, enterprise compliance, and secure model deployment standards.

• Active contributions to open‑source AI frameworks, technical publications, or relevant AI community projects.