Careers

Spotware Systems Ltd is looking for AI Engineers to join our core team. In this role, you will be responsible for developing, deploying, and integrating AI-driven solutions that serve as a backbone for both our internal processes and customer-facing products. You will work closely with our AI Team Lead and collaborate with cross-functional teams to build complete, production-ready AI systems. Given our dynamic environment, you must be adept at rapid prototyping and Agile development.

Responsibilities:

• Designing, developing, and implementing AI algorithms that drive our product innovations, including tools to accelerate internal productivity and enhance trader functionalities.
• Creating initiatives that automate core processes, significantly reducing time-to-market and operational overhead.
• Integrating AI systems into existing products (e.g., the cTrader platform) by building robust, RESTful APIs using FastAPI.
• Actively participating in Agile/Scrum processes, including sprint planning, daily stand-ups, and retrospectives.
• Staying current with AI trends and emerging frameworks, libraries, and other technologies.

• Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field.
• Minimum of 2 years of hands-on experience as an AI Engineer, Machine Learning Engineer, or a similar role with a proven track record in building, deploying, and integrating AI/ML systems into production environments.
• Expert-level proficiency in Python, including asynchronous programming and advanced object-oriented design.
• Strong experience with FastAPI, RESTful API development, and integration techniques.
• A solid working knowledge of LLMs — understanding their structure, operation, and application — is mandatory.
• Familiarity with document databases, SQL, and general database principles.

Preferred Qualifications:

• Extensive familiarity with modern AI frameworks such as LangChain and LangGraph, with proven proficiency in using the related service LangSmith.
• Strong understanding of agentic principles (e.g., the TAO cycle).
• Experience with additional AI frameworks or libraries beyond those specified.
• Proficiency in advanced evaluation techniques for AI systems, particularly for language models (e.g., LM-as-a-Judge methodologies, evaluation frameworks like Opik, OpenEvals, or AgentEvals).
• Comprehensive knowledge of RAG techniques and methodologies, including chunking, embeddings, and the effective use of vector databases.
• Prior experience working in finance or trading platforms.
• Exposure to UI development using frameworks such as Streamlit or Gradio.
• Experience with fine-tuning models (e.g., using LoRA or QLoRA).
• Familiarity with the Model Context Protocol (MCP).
• Ability to leverage AI-assisted development tools such as Cursor, Cline, and GitHub Copilot.