Requirements: English
Company: CODA100
Region: Ldz , Ldz Voivodeship
CODA100 is seeking a talented and versatile AI Developer/Engineer to design, develop, and implement AI-driven solutions that leverage Machine Learning (ML), Large Language Models (LLMs), and Natural Language Processing (NLP). This role combines hands-on technical work with opportunities to collaborate, innovate, and create impactful solutions tailored to real-world business challenges. You will work with cutting-edge technologies like Retrieval-Augmented Generation (RAG) and modern deployment tools to deliver production-ready systems that drive meaningful change.
Project Scope:
As an AI Developer/Engineer, you will focus on building, deploying, and optimizing AI-powered solutions. This includes:
- Experimenting with Large Language Models, Generative AI frameworks and advanced search technologies to design and deploy scalable solutions for tasks such as text generation, summarization, conversational AI, and recommendation systems.
- Developing and managing APIs that connect AI capabilities with external systems.
- Collaborating with clients and stakeholders to identify AI-driven opportunities and create tailored solutions.
- Delivering training sessions, workshops, or presentations to share AI insights and demonstrate solutions.
- Designing scalable AI architectures and deploying them using containerization tools like Docker and Kubernetes.
Duties
Deliverables:
- Build, fine-tune, and deploy AI and LLM models for tasks such as text generation, summarization, and semantic search.
- Implement Retrieval-Augmented Generation (RAG) workflows to enhance AI performance using vector databases and embeddings.
- Develop robust APIs for seamless integration of AI capabilities into business workflows.
- Use cloud platforms (e.g., AWS, Azure) for AI solution deployment and scaling.
- Optimize and deploy containerized AI solutions with tools like Docker and Kubernetes.
- Create presentations, documentation, and training materials to support knowledge-sharing and solution adoption.
Responsibilities:
- AI Solution Development: Design, implement, and test AI models, focusing on Machine Learning, Generative AI, and LLMs.
- RAG Integration: Build workflows leveraging Retrieval-Augmented Generation to enhance search and recommendation capabilities.
- API Development: Develop and maintain scalable APIs for AI models to ensure easy integration and usability.
- Cloud Deployment: Deploy AI applications on AWS, Azure, or similar platforms, ensuring reliability and scalability.
- Containerization: Use Docker and Kubernetes to manage containerized applications and streamline deployment processes.
- Knowledge Sharing: Lead or support training sessions, workshops, or presentations to share AI best practices and insights with teams and clients.
- Collaboration: Work closely with cross-functional teams to identify AI opportunities and deliver tailored solutions.
- Continuous Improvement: Monitor and refine deployed solutions to ensure optimal performance and alignment with business objectives.
Required skills
Skills and Qualifications:
Education
- Bachelors degree in Computer Science, Artificial Intelligence, Data Science, or a related field; a Masters degree is a plus.
Experience
- Practical experience with Large Language Models (LLMs), Generative AI frameworks, and Machine Learning (ML) techniques.
- Hands-on experience in building and deploying AI solutions using modern frameworks and cloud platforms.
- Familiarity with Retrieval-Augmented Generation (RAG) workflows, vector databases, and embeddings.
- Proven ability to plan and deliver trainings, workshops, or presentations related to AI technologies.
Technical Skills
- Strong proficiency in Python for AI development.
- Experience with AI and ML frameworks such as TensorFlow, PyTorch, or Hugging Face Transformers.
- Knowledge of Natural Language Processing (NLP) concepts and tools (e.g., spaCy, NLTK).
- Familiarity with containerization tools like Docker and orchestration platforms like Kubernetes.
- Experience with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform (GCP).
- Proficiency in developing and deploying APIs for AI models and solutions.
Preferred Skills
- Familiarity with prompt engineering techniques (e.g., zero-shot, few-shot prompting).
- Understanding of data pipelines and tools for data preprocessing and feature engineering.
- Hands-on experience in training and fine-tuning open-source models using tran