Technical Architect- Artificial Intelligence

As a Senior AI/ML Systems Developer, your primary responsibility will involve designing, developing, and deploying end-to-end AI/ML systems with a specific focus on large language models (LLMs), prompt engineering, and scalable system architecture. Leveraging technologies such as Java, Node.js, and .NET, you will be tasked with building robust and high-performance solutions that seamlessly integrate with enterprise systems. To excel in this role, you should hold a Bachelor's or Master's degree in Computer Science, Engineering, or a related field, with a PhD being a plus. Additionally, you should possess over 10 years of experience in AI/ML development, including at least 2 years of hands-on experience working on LLMs or NLP. Your expertise should extend to end-to-end system design and deployment of production-grade AI systems, with a strong command over Java, Node.js, and .NET for backend development. Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, and Hugging Face Transformers is also essential. Your key responsibilities will include: 1. Model Development & Training: Designing, training, and fine-tuning LLMs for tasks like natural language understanding, generation, and classification. Implementing and optimizing machine learning algorithms utilizing frameworks like TensorFlow, PyTorch, or Hugging Face. 2. Prompt Engineering: Crafting high-quality prompts to enhance LLM performance for specific use cases, like chatbots, text summarization, and question-answering systems. Experimenting with prompt tuning and few-shot learning techniques to enhance model accuracy and efficiency. 3. End-to-End System Design: Architecting scalable, secure, and fault-tolerant AI/ML systems by integrating LLMs with backend services and APIs. Developing microservices-based architectures using Java, Node.js, or .NET for seamless integration with enterprise applications. Designing and implementing data pipelines for preprocessing, feature engineering, and model inference. 4. Integration & Deployment: Deploying ML models and LLMs in production environments via containerization (Docker, Kubernetes) and cloud platforms (AWS, Azure, GCP). Building RESTful or GraphQL APIs to expose AI capabilities to front-end or third-party applications. 5. Performance Optimization: Optimizing LLMs for latency, throughput, and resource efficiency using techniques like quantization, pruning, and model distillation. Monitoring and enhancing system performance through logging, metrics, and A/B testing. 6. Collaboration & Leadership: Collaborating closely with data scientists, software engineers, and product managers to align AI solutions with business objectives. Mentoring junior engineers and contributing to best practices for AI/ML development. You will be excited by the opportunity to lead AI innovation in a fast-growing, technology-driven organization, working on cutting-edge AI solutions such as LLMs, autonomous AI agents, and Generative AI applications. Engaging with top-tier enterprise clients and driving AI transformation at scale will be key aspects of your role. Location: Ahmedabad,