Job
Description
As an Applied AI Engineer, you will work at the intersection of AI research and practical implementation. You will develop machine learning (ML) and deep learning (DL) models, integrate them into our SaaS platform, and optimize them for scalability, performance, and business impact. Key Responsibilities: Data Engineering & Feature Engineering: Work with structured and unstructured data to build high-quality datasets. Develop robust feature engineering pipelines to improve model accuracy. Implement data preprocessing and augmentation techniques. MLOps & AI Infrastructure: Build and maintain ML pipelines for continuous integration and deployment (CI/CD). Implement model monitoring, retraining, and performance tracking frameworks. Work with cloud platforms (AWS, GCP, Azure) for AI model deployment and scaling. AI Integration in SaaS Applications: Collaborate with software engineers to integrate AI models into customer-facing SaaS products. Develop APIs and microservices for seamless AI-powered functionalities. Optimize inference performance for real-time and batch processing scenarios. Collaboration & Research: Stay updated with the latest AI research and bring innovative solutions to production. Work closely with product managers, designers, and engineers to align AI capabilities with business goals. Participate in code reviews, knowledge sharing, and AI/ML best practices. Prompt Engineering: Design, develop, and refine AI-generated text prompts for various applications to ensure accuracy, engagement, and relevance Craft and optimize prompts that guide our AI systems to generate accurate, informative, and creative outputs Build accessible libraries of prompts, keywords, and syntax guidelines for optimal query results Develop robust evaluation frameworks to assess AI model performance and prompt effectiveness Test and analyze outputs by experimenting with different prompts and measuring against defined metrics Create dashboards and reporting tools to track AI performance across multiple dimensions Apply human judgment to identify gaps in AI-generated output and refine prompts accordingly Implement continuous improvement processes based on evaluation insights What Were Looking For: Experience: 5-6 years in AI/ML engineering, with hands-on experience in Generative AI, NLP and deploying AI solutions at scale. Technical Expertise: Strong background in machine learning, deep learning, and NLP. Proficiency in Python Help assess performance metrics, develop novel agent frameworks, create and oversee data workflows, and conduct extensive testing to deploy innovative capabilities. Ship with high intent and work with the team to improve your ability to iterate and ship AI-powered features over time. Experience with data processing tools (Pandas, NumPy, Spark, Dask). Familiarity with MLOps, CI/CD, and cloud platforms (AWS SageMaker, GCP Vertex AI, Azure ML). AI Deployment & Optimization: Experience with optimizing models for performance, interpretability, and real-time applications. Strong Problem-Solving Skills: Ability to translate business problems into AI-driven solutions. SaaS Experience (Preferred): Understanding of how AI enhances SaaS applications and workflows.
