AI Engineer

Responsibilities
• Lead the design, development, and deployment of SaaS products leveraging LLMs, including platforms like Google, Azure OpenAI, Meta, Claude, or Mistral and Open-source models.
• Support product lifecycle, from conceptualization to deployment, ensuring seamless integration of AI models with business requirements and user needs.
• Build Conversational AI, BOTS, Train and Re-Train them.
• Knowledge of Security Implementation, Pattern Analysis, OCR, Facial Recognition, Fraud detection leveraging AI
• Build secure, scalable, and efficient Solutions 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.
• Working with Various types of Data(Speech, Vision, Spatial, IoT) and training the models.
• 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.
• Any Banking domain experience is added advantage

Requirements