Machine Learning Engineer – Generative AI (LLMs / RAG / Agentic AI)

Stellar Technologies; Abu Dhabi; On-site; AED 200,000 - 300,000; Full time

Job summary

A leading technology firm is seeking a Machine Learning Engineer (GenAI) to design, build, and deploy next-generation AI systems. The role involves developing scalable AI pipelines, integrating real-time APIs, and delivering intelligent solutions. Ideal candidates should have strong experience with LLMs, Python, and container orchestration technologies. This position offers the chance to work at the intersection of machine learning and cloud infrastructure in a collaborative environment.

Qualifications

• Strong hands-on experience with LLMs, RAG, and agentic frameworks.
• Proficiency in Python with deep understanding of ML libraries.
• Solid experience in API and microservices engineering.

Responsibilities

• Develop and optimize AI systems leveraging LLMs, RAG, and agentic frameworks.
• Build and deploy production-grade ML pipelines.
• Design and manage APIs and streaming services.

Skills

LLMs, RAG, and agentic frameworks

Python

API and microservices engineering

Azure cloud platforms

Containerization and orchestration

Observability and monitoring tools

Tools

Docker

Kubernetes

PyTorch

TensorFlow

Hugging Face Transformers

Job description

Role Summary

Stellar Technologies is seeking a Machine Learning Engineer (GenAI) to design, build, and deploy next‑generation AI systems combining Large Language Models (LLMs), Retrieval‑Augmented Generation (RAG), and agentic AI frameworks.

In this role, you will bridge model development and production engineering — developing scalable AI pipelines, integrating real‑time APIs, and ensuring high‑performance AI services that power enterprise‑grade solutions. You will work at the intersection of machine learning, cloud infrastructure, and applied research, collaborating with top engineers and data scientists to deliver intelligent, production‑ready AI capabilities.

Key Responsibilities

• Develop and optimize AI systems leveraging LLMs, RAG, and agentic AI frameworks (LangChain, LangGraph).

• Build and deploy production‑grade ML pipelines with real‑time inference and retrieval components.

• Design and manage APIs and streaming services to integrate AI models into enterprise platforms.

• Implement containerized, orchestrated deployments using Docker, Kubernetes, and Azure ML.

• Automate data preprocessing, model training, evaluation, and versioning pipelines.

• Collaborate with cross‑functional teams to integrate models into front‑end, analytics, and automation workflows.

• Ensure governance, compliance, and security of deployed AI workloads.

• Conduct performance benchmarking and optimize inference latency and cost.

• Monitor AI systems in production using observability frameworks (logging, metrics, tracing).

• Participate in architecture discussions to enhance scalability and reliability of AI services.

Required Skills & Experience

• Strong hands‑on experience with LLMs, RAG, and agentic frameworks (LangChain, LangGraph, Semantic Kernel, etc.).

• Proficiency in Python, with deep understanding of ML libraries like PyTorch, TensorFlow, scikit‑learn, Hugging Face Transformers.

• Solid experience in API and microservices engineering (FastAPI, Flask).

• Familiarity with streaming architectures and real‑time data handling.

• Knowledge of cloud platforms (Azure preferred), including Azure AI, Cognitive Services, and ML Ops.

• Experience with containerization and orchestration (Docker, Kubernetes).

• Understanding of vector databases (Pinecone, Weaviate, FAISS) and retrieval mechanisms.

• Experience in CI/CD, model deployment, and production monitoring.

Preferred Skills

• Exposure to GPU‑based inference optimization and serverless deployment.

• Knowledge of observability and monitoring tools for AI (Prometheus, Grafana, Azure Monitor).

• Experience in model fine‑tuning, prompt engineering, or agentic orchestration.

• Understanding of AI governance, ethical AI, and data privacy frameworks.

Soft Skills

• Strong analytical and problem‑solving mindset.

• Excellent collaboration and communication skills.

• Passion for innovation, experimentation, and applied AI.