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

Stellar Technologies; Abu Dhabi; On-site; AED 260,000 - 480,000; Full time

Job summary

Stellar Technologies is seeking a Machine Learning Engineer (GenAI) to design, build, and deploy next-generation AI systems leveraging LLMs, RAG, and agentic AI frameworks. You will bridge model development and production engineering to deliver scalable, production-ready AI capabilities for enterprise platforms.

Join a collaborative team of engineers and data scientists, working across cloud infrastructure, model deployment, and real-time APIs to drive governance, performance, and cost

Qualifications

• Strong hands-on experience with LLMs, RAG, and agentic frameworks.

Responsibilities

• Develop and optimize AI systems leveraging LLMs, RAG, and agentic frameworks.
• Build production-grade ML pipelines with real-time inference and retrieval components.
• Design and manage APIs and streaming services to integrate models into enterprise platforms.
• Implement containerized deployments using Docker, Kubernetes, and Azure ML.
• Automate data preprocessing, model training, evaluation, and versioning.
• Collaborate with cross-functional teams to integrate models into front-end and analytics workflows.
• Ensure governance, compliance, and security of deployed AI workloads.
• Monitor AI systems in production using observability frameworks.
• Participate in architecture discussions to improve scalability and reliability.

Skills

LLMs, RAG & agentic frameworks

Python & ML libraries (PyTorch, Tensor

API & microservices engineering

Streaming architectures & real-time

Azure cloud & ML Ops

Containerization & orchestration (Dock

Vector databases & retrieval (Pinecone

CI/CD & production monitoring

Tools

Docker

Kubernetes

Azure ML

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.