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Information Technology 🏢 Full Time ⭐️ Verified

Lead AI/ML Engineer (2026 Vision)

Nexus Future Labs
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
Live Update
8 Juli 2026
Deadline
8 Jul 2027

Job Description

Are you ready to define the technological landscape of 2026? Nexus Future Labs is seeking a visionary Lead AI/ML Engineer to architect the next generation of generative intelligence systems. We are building the infrastructure for a world where AI seamlessly integrates into enterprise workflows.

In this role, you won't just write code; you will pioneer the protocols that define the future of human-machine interaction. We are looking for a builder who is obsessed with scalability, ethical AI, and pushing the boundaries of what is possible with Large Language Models (LLMs).

Responsibilities

  • Architect and deploy scalable Large Language Models (LLMs) and autonomous generative AI agents for production use.
  • Optimize model inference latency and throughput to ensure real-time performance at scale.
  • Lead a high-performing team of data scientists and ML engineers in researching cutting-edge NLP techniques.
  • Collaborate with product leadership to translate 2026 strategic roadmaps into technical reality.
  • Establish MLOps best practices, CI/CD pipelines, and rigorous model governance frameworks.
  • Drive experimentation with emerging architectures like Multimodal models and Agentic workflows.

Qualifications

  • 7+ years of experience in software engineering with a deep focus on Machine Learning and Deep Learning.
  • Expert proficiency in Python, PyTorch, and TensorFlow or JAX.
  • Proven track record of working with Transformer architectures, BERT, GPT, or similar NLP models.
  • Experience building and optimizing Retrieval-Augmented Generation (RAG) pipelines.
  • Strong grasp of cloud infrastructure (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
  • Excellent communication skills and the ability to mentor junior engineers.

Required Skills

Python PyTorch TensorFlow Machine Learning Deep Learning NLP Large Language Models LLM Cloud Computing Kubernetes MLOps Generative AI

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