Job Description
Shape the Intelligence of Tomorrow
We are seeking a visionary Senior Generative AI Engineer to spearhead our research and development for 2026. In this role, you will architect the next generation of Large Language Models (LLMs) and Generative AI applications that redefine user interaction. If you are passionate about pushing the boundaries of artificial intelligence and thrive in a fast-paced, high-impact environment, Nexus Future Systems is the place for you.
Join our elite team of data scientists and engineers to build scalable, safe, and transformative AI systems.
Responsibilities
- Architect Advanced AI Systems: Design and implement scalable Large Language Model (LLM) architectures and inference pipelines optimized for production environments.
- Retrieval-Augmented Generation (RAG): Develop sophisticated RAG pipelines to enhance model accuracy, reduce hallucinations, and integrate external knowledge bases.
- Model Fine-tuning: Utilize cutting-edge techniques (LoRA, QLoRA, P-Tuning) to fine-tune open-source foundation models (Llama, Mistral, Falcon) for specific enterprise domains.
- Inference Optimization: Optimize model latency and throughput using quantization, pruning, and ONNX Runtime to ensure real-time performance.
- AI Safety & Alignment: Implement robust guardrails and safety protocols to ensure AI outputs are ethical, unbiased, and compliant with evolving regulations.
- Collaboration & Innovation: Partner with cross-functional teams (Product, Data Science, Security) to translate complex business requirements into innovative AI technical solutions.
Qualifications
- Education: Master’s or PhD in Computer Science, Mathematics, or a related technical field (or equivalent extensive practical experience).
- Experience: 5+ years of software engineering experience, with at least 3 years specifically focused on Machine Learning, Deep Learning, or NLP.
- Technical Proficiency: Deep understanding of Transformer architectures, PyTorch, and TensorFlow. Proficiency in Python.
- Toolstack Mastery: Strong experience with Hugging Face, LangChain, LlamaIndex, and vector databases (Pinecone, Weaviate, Milvus).
- MLOps: Experience with CI/CD for ML, containerization (Docker, Kubernetes), and model versioning (MLflow, DVC).
- Problem Solving: Demonstrated ability to troubleshoot complex system bottlenecks and optimize performance under heavy load.