Applied AI Research Engineer

Role Overview: As an Applied Research Engineer at Accrete AI, you will work on designing, building, and operationalizing advanced AI systems. You will collaborate with AI scientists and engineering teams to transform research ideas into impactful models and workflows. Your role will involve working on NLP, computer vision, multimodal models, and agent-based systems, ensuring the transition from experimental prototypes to scalable, reliable components. Your expertise in hands-on engineering, knowledge of modern AI models and tooling, and ability to balance innovation with real-world constraints will be highly valued. Key Responsibilities: - Design, implement, and operationalize advanced AI models and agentic workflows, translating research ideas into robust, production-ready systems. - Collaborate closely with AI scientists to prototype, experiment, and evaluate models across various domains like NLP, computer vision, and multimodal learning. - Partner with machine learning engineers and application teams to integrate research artifacts into products, services, and platforms. - Build and optimize model pipelines, inference workflows, and tooling focusing on performance, scalability, and reliability. - Contribute to model evaluation, benchmarking, and validation, ensuring research outputs meet real-world constraints and quality standards. - Support the deployment, monitoring, and iteration of AI models to adapt to changing data, requirements, and environments. - Stay updated with advancements in AI and machine learning to enhance system capabilities. - Foster a culture of high-quality engineering through activities like code reviews, documentation, testing, and knowledge sharing. Qualification Required: - Bachelor's degree in Computer Science, Engineering, or related field with at least 2 years of hands-on experience in building AI/ML systems; or a Master's degree in a relevant field. - Proficient in Python with a strong background in implementing and maintaining AI or ML-driven systems. - Practical experience with modern AI models, including large language models and deep learning models for NLP or computer vision. - Experience in developing agentic or tool-using AI workflows, such as orchestration and pipeline-based execution. - Familiarity with model training, fine-tuning, and evaluation, collaborating effectively with research scientists. - Previous experience deploying AI or ML components in production or near-production environments, considering performance, reliability, and scaling aspects. - Ability to work collaboratively with machine learning engineers and application teams to operationalize research outputs. - Strong problem-solving skills, a quick learner of new models, tools, and systems, and clear communication skills for effective cross-functional teamwork. Note: The company overview was not included in the provided job description. Role Overview: As an Applied Research Engineer at Accrete AI, you will work on designing, building, and operationalizing advanced AI systems. You will collaborate with AI scientists and engineering teams to transform research ideas into impactful models and workflows. Your role will involve working on NLP, computer vision, multimodal models, and agent-based systems, ensuring the transition from experimental prototypes to scalable, reliable components. Your expertise in hands-on engineering, knowledge of modern AI models and tooling, and ability to balance innovation with real-world constraints will be highly valued. Key Responsibilities: - Design, implement, and operationalize advanced AI models and agentic workflows, translating research ideas into robust, production-ready systems. - Collaborate closely with AI scientists to prototype, experiment, and evaluate models across various domains like NLP, computer vision, and multimodal learning. - Partner with machine learning engineers and application teams to integrate research artifacts into products, services, and platforms. - Build and optimize model pipelines, inference workflows, and tooling focusing on performance, scalability, and reliability. - Contribute to model evaluation, benchmarking, and validation, ensuring research outputs meet real-world constraints and quality standards. - Support the deployment, monitoring, and iteration of AI models to adapt to changing data, requirements, and environments. - Stay updated with advancements in AI and machine learning to enhance system capabilities. - Foster a culture of high-quality engineering through activities like code reviews, documentation, testing, and knowledge sharing. Qualification Required: - Bachelor's degree in Computer Science, Engineering, or related field with at least 2 years of hands-on experience in building AI/ML systems; or a Master's degree in a relevant field. - Proficient in Python with a strong background in implementing and maintaining AI or ML-driven systems. - Practical experience with modern AI models, including large language mod