Fellowship Title: AI Engineer Fellow
Department: Engineering & LunarTech Labs
Location: Remote (Global) / Hybrid (depending on specific team hub)
Type: Fellowship
About LUNARTECH
At LUNARTECH, we don't just teach the future of technology—we build it. We are an AI & Data Science engineering powerhouse operating on two fronts: LunarTech Academy, where we democratize tech education for the next generation of innovators, and LunarTech Labs, where we design, build, and deploy production-grade AI systems for enterprise clients in heavy industry, healthcare, and telecom.
We are "Engineers, not slide decks." We reject the hype surrounding AI in favor of ROI-first, production-ready systems. We are looking for an AI Engineer Fellow who shares our obsession with uncompromised excellence, scientific rigor, and building intelligent systems that deliver real-world impact.
The Role
As an AI Engineer Fellow, you will be embedded at the core of our mission—gaining hands-on experience designing, training, and deploying machine learning models that solve complex problems for our enterprise clients and power our educational platforms. You will not just experiment with models; you will learn to own the entire lifecycle from research and prototyping to production deployment and continuous improvement.
You will work on cutting-edge problems in NLP, computer vision, and predictive analytics, ensuring our AI solutions are robust, explainable, and production-ready.
Key Responsibilities
Model Development: Design, train, and optimize machine learning models (deep learning, traditional ML, LLMs) for real-world applications. You will move beyond Jupyter notebooks to build models that perform reliably in production.
Production ML Systems: Build end-to-end ML pipelines that handle data ingestion, feature engineering, model training, evaluation, and deployment. You will own the full stack from data to inference.
LLM Engineering: Develop and fine-tune Large Language Model applications including prompt engineering, RAG systems, and agent frameworks. You will stay at the forefront of the rapidly evolving LLM landscape.
Research & Innovation: Evaluate emerging techniques and determine their applicability to our business problems. You will translate academic research into practical, deployable solutions.
Model Optimization: Optimize models for inference speed, memory efficiency, and cost. You will ensure our AI solutions are economically viable at scale.
Explainability & Ethics: Implement model explainability and fairness techniques. You will ensure our AI systems are transparent, unbiased, and trustworthy—especially in high-stakes domains like healthcare.
Collaboration: Work closely with Data Engineers, DevOps, and Product teams to deliver integrated AI solutions. You will translate business requirements into technical specifications.
What We Are Looking For
Engineering Background: Foundational experience in Machine Learning Engineering or Applied AI through coursework, research projects, or prior internships. Familiarity with shipping ML models to production and understanding the difference between research and real-world deployment is a plus.
Deep Learning Expertise: Strong proficiency in PyTorch or TensorFlow. You understand neural network architectures deeply—not just how to call APIs.
LLM Experience: Hands-on experience with LLMs, including fine-tuning, prompt engineering, RAG architectures, and frameworks like LangChain or LlamaIndex.
Software Engineering Skills: You write clean, tested, production-quality code (Python). You understand software engineering best practices and can build maintainable ML systems.
MLOps Awareness: Familiarity with ML infrastructure tools (MLflow, Weights & Biases, Kubeflow) and deployment patterns (model serving, A/B testing, monitoring).
Mathematical Foundation: Strong understanding of statistics, linear algebra, and optimization. You can read and implement techniques from academic papers.
Communication: Ability to explain complex technical concepts to non-technical stakeholders. You can bridge the gap between AI capabilities and business value.
Bonus Points
Experience with domain-specific AI (healthcare, energy, telecommunications).
Publications or contributions to the ML research community.
Experience building and deploying multi-modal AI systems.
Background in edge deployment and model compression techniques (quantization, pruning, distillation).
Experience with reinforcement learning or agent-based systems.
Why Join the LUNARTECH Fellowship?
Impact: Work on AI systems that actually reach production and solve real-world problems in critical industries.
Mentorship: Receive dedicated mentorship from senior AI engineers and direct exposure to production-grade ML systems.
Growth: Unlimited access to the entire LunarTech Academy catalog. We invest heavily in your upskilling because we believe education is a human right.
Culture: A remote-first, diverse global team that values integrity and performance. No micromanagement; just high standards and the support to meet them.
Fellowship Stipend: Competitive stipend provided for the duration of the fellowship, plus potential for full-time conversion based on performance.
Type: Fellowship
Work Location: Remote / Hybrid
Job Types: Full-time, Internship
Work Location: Remote