Junior Strong/Middle AI/ML Engineer

We are looking for an AI/ML Engineer who is passionate about building intelligent products powered by

modern machine learning and generative AI technologies. In this role, you will work across the entire

development lifecycle — from research and prototyping to deployment and production support.

What You'll Do

- Design and implement AI-driven solutions using modern LLM providers such as OpenAI, Anthropic, and Mistral.

- Build and optimize Retrieval-Augmented Generation (RAG) systems, including document

processing, embeddings, and retrieval workflows.

- Develop applications using LangChain, LlamaIndex, and related AI frameworks.

- Create and maintain backend services in Python using FastAPI or Flask.

- Train, fine-tune, and integrate machine learning and deep learning models.

- Deliver NLP solutions for tasks such as text classification, entity extraction, summarization, and

information processing.

- Participate in computer vision initiatives when required by the project.

- Work with relational, NoSQL, and vector databases.

- Deploy, monitor, and support AI services in cloud environments using modern DevOps

practices.

What We're Looking For

- 2+ years of commercial Python development experience.

- Practical experience working with Large Language Models and prompt engineering techniques.

- Strong understanding of embeddings, retrieval strategies, context windows, and RAG

architectures.

- Experience building APIs with FastAPI or Flask, including async programming and

SQLAlchemy.

- Hands-on experience with ML/DL frameworks such as PyTorch, TensorFlow, or scikit-learn.

- Solid knowledge of NLP tools and ecosystems, including Hugging Face and Transformers.

- Experience working with SQL, NoSQL, and vector databases.

- Familiarity with Docker, CI/CD pipelines, and cloud platforms.

- Commitment to code quality through testing, typing, and development best practices.

- Upper-Intermediate English level or higher.

Nice to Have

- Experience with Computer Vision technologies such as OpenCV, YOLO, EfficientDet, or similar

frameworks.

- Understanding of hybrid search approaches combining vector and keyword-based retrieval.

-Exposure to Kubernetes, monitoring, and observability tools.