Job Description
Are you ready to engineer the future of intelligence?
At FutureScale Solutions, we are not just keeping up with the pace of technology; we are defining it. As we look toward the horizon of 2026, we are seeking a visionary Senior AI Engineer to lead our next-generation Generative AI initiatives. You will be at the forefront of developing scalable, ethical, and high-impact AI systems that solve complex real-world problems.
In this pivotal role, you will architect robust machine learning pipelines, fine-tune large language models (LLMs), and ensure our AI solutions are secure, efficient, and aligned with our enterprise vision. If you thrive in a fast-paced, innovative environment and want to build the tools that will define the next era of technology, we want to hear from you.
Responsibilities
- Architect & Develop: Design and implement scalable AI/ML infrastructure and algorithms using Python, PyTorch, and TensorFlow.
- Model Optimization: Lead the fine-tuning and optimization of LLMs (e.g., GPT-4, LLaMA) to ensure high performance and low latency in production environments.
- Research & Innovation: Stay ahead of the curve on emerging AI trends, including Retrieval-Augmented Generation (RAG), vector databases, and multimodal models.
- System Integration: Integrate AI models into existing software ecosystems, working closely with backend and frontend teams to deliver seamless user experiences.
- Ethical AI: Establish guidelines and best practices for responsible AI development, ensuring fairness, transparency, and data privacy.
- Mentorship: Mentor junior engineers and data scientists, fostering a culture of continuous learning and technical excellence.
Qualifications
- Experience: 5+ years of professional experience in software engineering, machine learning, or data science.
- Technical Stack: Proficiency in Python, SQL, and experience with cloud platforms (AWS, GCP, or Azure).
- AI Expertise: Deep understanding of machine learning principles, neural networks, and natural language processing (NLP).
- Tools: Hands-on experience with MLOps tools (Docker, Kubernetes), version control (Git), and model deployment frameworks.
- Education: Bachelor’s or Master’s degree in Computer Science, Mathematics, or a related technical field.
- Communication: Exceptional ability to translate complex technical concepts into clear, actionable insights for non-technical stakeholders.