Artificial Intelligence Engineer

Job Title: AI Engineer Want to join a startup, but with the stability of a larger organization? Join our innovation team at HGS that's focused on building SaaS products. If you are highly driven & passionate person who'd like to build highly scalable SaaS products in a startup type of environment, you're welcome to apply. The HGS Digital Innovation Team is designed to create products and solutions relevant for enterprises, discover innovations and to contextualize and experiment with them within a specific industry. This unit provides an environment for the exploration, development, and testing of Cloud-based Digital AI solutions. In addition to that it also looks at rapid deployment at scale and sustainability of these solutions for target business impacts. Job Overview We are seeking an agile AI Engineer with a strong focus on both AI engineering and SaaS product development in a 0-1 product environment. This role is perfect for a candidate skilled in building and iterating quickly, embracing a fail fast approach to bring innovative AI solutions to market rapidly. You will be responsible for designing, developing, and deploying SaaS products using advanced Large Language Models (LLMs) such as Meta, Azure OpenAI, Claude, and Mistral, while ensuring secure, scalable, and high-performance architecture. Your ability to adapt, iterate, and deliver in fast-paced environments is critical. Responsibilities - Lead the design, development, and deployment of SaaS products leveraging LLMs, including platforms like Meta, Azure OpenAI, Claude, and Mistral. - Support product lifecycle, from conceptualization to deployment, ensuring seamless integration of AI models with business requirements and user needs. - Build secure, scalable, and efficient SaaS products that embody robust data management and comply with security and governance standards. - Collaborate closely with product management, and other stakeholders to align AI-driven SaaS solutions with business strategies and customer expectations. - Fine-tune AI models using custom instructions to tailor them to specific use cases and optimize performance through techniques like quantization and model tuning. - Architect AI deployment strategies using cloud-agnostic platforms (AWS, Azure, Google Cloud), ensuring cost optimization while maintaining performance and scalability. - Apply retrieval-augmented generation (RAG) techniques to build AI models that provide contextually accurate and relevant outputs. - Build the integration of APIs and third-party services into the SaaS ecosystem, ensuring robust and flexible product architecture. - Monitor product performance post-launch, iterating and improving models and infrastructure to enhance user experience and scalability. - Stay current with AI advancements, SaaS development trends, and cloud technology to apply innovative solutions in product development. Qualifications - Bachelor’s degree or equivalent in Information Systems, Computer Science, or related fields. - 6+ years of experience in product development, with at least 2 years focused on AI-based SaaS products. - Demonstrated experience in leading the development of SaaS products, from ideation to deployment, with a focus on AI-driven features. - Hands-on experience with LLMs (Meta, Azure OpenAI, Claude, Mistral) and SaaS platforms. - Proven ability to build secure, scalable, and compliant SaaS solutions, integrating AI with cloud-based services (AWS, Azure, Google Cloud). - Strong experience with RAG model techniques and fine-tuning AI models for business-specific needs. - Proficiency in AI engineering, including machine learning algorithms, deep learning architectures (e.G., CNNs, RNNs, Transformers), and integrating models into SaaS environments. - Solid understanding of SaaS product lifecycle management, including customer-focused design, product-market fit, and post-launch optimization. - Excellent communication and collaboration skills, with the ability to work cross-functionally and drive SaaS product success. - Knowledge of cost-optimized AI deployment and cloud infrastructure, focusing on scalability and performance.