AI Engineer
Role details
Job location
Tech stack
Job description
As a core member of the engineering team, you will design and implement AI-driven applications including retrieval-augmented systems, conversational agents, document intelligence solutions, and generative content tools. You will collaborate closely with product, data, and strategy teams to translate business objectives into scalable AI architectures., * Design and deploy machine learning and LLM-based solutions across use cases such as document processing (OCR), RAG pipelines, AI agents, and generative applications.
- Develop, test, and refine prompt strategies to improve output quality and alignment with business requirements.
- Evaluate, fine-tune, and benchmark ML and language models using structured experimentation and performance metrics.
- Build and maintain data pipelines for model training, evaluation, and deployment workflows.
- Collaborate cross-functionally to integrate AI capabilities into client-facing platforms and internal tools.
- Deploy models to production environments and monitor system performance, reliability, and cost efficiency.
- Stay current with advancements in generative AI, deep learning, and emerging frameworks to continuously enhance system capabilities.
Requirements
Do you have experience in SQL databases?, Do you have a Master's degree?, The ideal candidate has strong experience working with leading commercial and open-source LLM ecosystems and understands how to optimize, evaluate, and productionize AI systems responsibly and efficiently., * 5+ years of experience in AI/ML engineering or related roles.
- Bachelors or Masters degree in Computer Science, Engineering, or a related technical discipline.
- Hands-on experience working with major LLM platforms (commercial APIs and open-source models).
- Strong background building retrieval-augmented generation systems, LLM pipelines, or agent-based architectures.
- Proficiency in Python and experience with deep learning frameworks such as PyTorch (or equivalent).
- Solid foundation in machine learning, neural networks, and natural language processing.
- Experience evaluating model performance using appropriate validation and benchmarking techniques.
- Experience working with relational databases (e.g., PostgreSQL) and modern data warehouse systems.
- Strong analytical and problem-solving skills.
- Ability to communicate effectively across technical and non-technical stakeholders.
Preferred Experience
- Contributions to open-source AI projects or involvement in research communities.
- Experience deploying AI systems on major cloud platforms (AWS, GCP, or Azure).
- Familiarity with distributed data processing technologies (e.g., Spark or similar frameworks).
- Exposure to domain applications in sectors such as financial services, healthcare, retail, or enterprise technology.