About Krita.ai

We’re building an AI Agentic platform to automate complex enterprise workflows by combining large language models, fine tuned small models, business logic, and deep system integrations.

About the Role:

In this role, you will be at the forefront of developing and deploying cutting-edge AI solutions that directly impact our business. You will leverage your expertise in data and machine learning, natural language processing (NLP), and Agentic AI, to build scalable and robust systems that drive innovation and efficiency. You will be responsible for the entire AI lifecycle, from data acquisition and preprocessing to model development, deployment, and monitoring.

Responsibilities:

Data and ML Engineering:

• Design and implement robust data pipelines to extract, transform, and load (ETL) data from diverse structured and unstructured sources (e.g., databases, APIs, text documents, images, videos).
• Develop and maintain scalable data storage and processing solutions.
• Perform comprehensive data cleaning, validation, and feature engineering to prepare data for machine learning models.
• Build and deploy machine learning models for a variety of business applications, including but not limited to process optimization and enterprise efficiency.
• Web Scraping and Document Processing: Implement web scraping solutions and utilize document processing libraries to extract and process data from various sources.
• NLP and Computer Vision: Develop and implement NLP models for tasks such as text classification, sentiment analysis, entity recognition, and language generation. Implement computer vision models for image classification, object detection, and image segmentation.

Agentic AI Development:

• Design and develop highly scalable production-ready code for agentic AI systems.
• Implement and integrate agentic AI solutions into existing workflows to automate complex tasks and improve decision-making.
• Develop and maintain agentic systems for data wrangling, supply chain optimization, and enterprise efficiency projects.
• Work with LLMs, and other related technologies to create agentic workflows.
• Integrate NLP and Computer Vision capabilities into agentic workflows to enhance their ability to understand and interact with diverse data sources.

Model Development and Deployment:

• Design and develop machine learning models and algorithms to solve simplified business problems.
• Evaluate and optimize model performance through rigorous testing and experimentation.
• Deploy and monitor machine learning models in production environments.
• Implement best practices for model versioning, reproducibility, and explainability.
• Optimize and deploy NLP and computer vision models for real-time inference.

Communication and Collaboration:

• Clearly articulate complex technical concepts to both technical and non-technical audiences.
• Demonstrate live coding proficiency and effectively explain your code and design decisions.
• Collaborate with cross-functional teams, including product managers, data scientists, and software engineers.
• Document code, models, and processes for knowledge sharing and maintainability.

Qualifications:

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Natural Language Processing, Computer Vision, or a related field.
• Proven experience in developing and deploying machine learning models, NLP models, and computer vision models, and data pipelines.
• Strong programming skills in Python and experience with relevant libraries (e.g., TensorFlow, PyTorch, scikit-learn, pandas, NumPy, Hugging Face Transformers, OpenCV, Pillow).
• Experience with cloud computing platforms (e.g., AWS, GCP, Azure).
• Experience with database technologies (e.g., SQL, NoSQL).
• Experience with agentic AI development and LLMs is highly desirable.
• Product Engineering background
• Ability to demonstrate live coding proficiency.
• Experience in productionizing ML models.

Preferred Qualifications:

• Experience with containerization and orchestration technologies (e.g., Docker, Kubernetes).
• Experience with MLOps practices and tools.
• Experience with building RAG systems.
• Experience with deploying and optimizing models for edge devices.
• Experience with video processing and analysis.