Job Description
Are you ready to build the technology of tomorrow? At FutureForge Technologies, we are already engineering solutions for the 2026 tech landscape and beyond. We are seeking a visionary Lead Generative AI Engineer to spearhead our next-generation artificial intelligence initiatives.
In this high-impact role, you will move beyond traditional machine learning to architect autonomous, scalable, and ethical GenAI ecosystems. If you are passionate about pushing the boundaries of Large Language Models (LLMs), multi-modal AI, and autonomous agents, this is your opportunity to leave a lasting mark on the industry. Join us in San Francisco and help us forge the future.
Responsibilities
- Architect, develop, and deploy advanced Generative AI models and LLM-based systems tailored for our 2026 product roadmap.
- Lead a high-performing team of machine learning engineers, fostering a culture of innovation, rapid prototyping, and technical excellence.
- Design robust MLOps pipelines for continuous training, fine-tuning, and deployment of AI models at an enterprise scale.
- Collaborate with cross-functional teams including product managers, data scientists, and software engineers to integrate AI capabilities seamlessly.
- Ensure all AI solutions are developed with a strong emphasis on security, ethical AI practices, and responsible scaling.
- Stay ahead of industry trends, evaluating emerging AI frameworks and incorporating cutting-edge research into our technical stack.
- Optimize model performance, latency, and infrastructure costs across cloud environments.
Qualifications
- Master's or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a related technical field.
- 5+ years of hands-on experience in machine learning, with at least 2 years specifically focused on Generative AI and Large Language Models.
- Deep proficiency in Python and modern ML frameworks such as PyTorch, TensorFlow, or JAX.
- Proven experience building and scaling MLOps pipelines on major cloud platforms (AWS, GCP, or Azure).
- Strong understanding of transformer architectures, RAG (Retrieval-Augmented Generation), and prompt engineering.
- Excellent leadership and communication skills, with the ability to translate complex technical concepts to non-technical stakeholders.
- Experience with containerization and orchestration tools (Docker, Kubernetes).