Job Description

**About Tek4Real**

Tek4Real is a technology consulting and software engineering company
headquartered in Hong Kong and Denver, Colorado. We build modern digital
products — web, mobile, cloud, and AI-powered platforms — for startups,
growing businesses, and enterprises, working closely with clients across
regions.

**The Role**

We're looking for a Senior AI/ML Engineer with a deep understanding of AI/ML
principles and hands-on focus on Generative AI and Agentic AI. You'll
architect and ship production-grade GenAI systems for real clients, own
complex technical decisions, and set the standard for how we build AI across
projects.

This is a senior, high-ownership role — not an entry point into ML. We want
someone who has already built and deployed AI products and can lead.

**Hard Requirements (non-negotiable)**

* Fluent English communication — you'll work directly with U.S.-based clients.
* Availability to work in U.S. time zones — overlapping hours with U.S. clients and our engineering team.
* Located in the United States — candidates outside the U.S. will not be considered.
* Proven, production experience building and deploying ML/AI systems.

**What You 'll Do**

* Design, build, and deploy Generative AI and Agentic AI systems end to end.
* Build agentic workflows — planning, tool use, multi-step reasoning, and orchestration.
* Work with LLMs, embeddings, RAG, fine-tuning, and evaluation pipelines.
* Take AI products from prototype to production, with monitoring and optimization.
* Translate ambiguous client problems into well-defined technical solutions.
* Set MLOps practices and engineering standards; mentor other engineers.
* Collaborate with full-stack engineers, designers, and clients to ship complete products.

**What We 're Looking For**

* Deep understanding of core AI/ML principles — not just API calls, but how and why models work.
* Strong focus and hands-on experience in Generative AI and Agentic AI.
* Expert Python and major ML frameworks (PyTorch, TensorFlow, or similar).
* Experience with LLM orchestration frameworks (LangChain, LlamaIndex, agent frameworks, or equivalents).
* Solid grasp of the full ML lifecycle: data, training, evaluation, deployment, monitoring.
* Familiarity with cloud platforms (AWS, GCP, or Azure) and containerization.

**Nice to Have**

* Vector databases and retrieval systems.
* MLOps tooling (MLflow, Weights & Biases, Kubeflow).
* Inference optimization and model serving at scale.
* Open-source contributions, published work, or a strong GitHub portfolio.