Overview: Do you thrive on pushing the boundaries of artificial intelligence and creating impactful solutions?
We're looking for an innovative AI Software Engineer to design and implement cutting-edge AI/ML systems—ranging from advanced chatbots and AI agents to retrieval-augmented generation (RAG) systems and other machine learning applications.
Our mission is to enhance user experiences, streamline operations, and drive meaningful innovation through technology.
This role is ideal for engineers who excel at turning bold ideas into transformative AI solutions.
It's not just about technical expertise—it's about thinking fast, adapting even faster, and connecting the dots to tackle challenges creatively.
Collaboration and communication are key, so we're seeking team players who embrace change, bring fresh perspectives, and use their talents to craft high-performance, scalable systems.
If this resonates with you, we'd love to have you onboard!
Responsibilities: AI Solution Development: Design and implement advanced AI/GenAI solutions, such as chatbots, AI agents, and Retrieval-Augmented Generation (RAG) systems, to enhance user experiences and operational efficiency. Code Excellence: Write clean, high-performance, and maintainable code, adhering to industry best practices. System Optimization: Evaluate and optimize AI systems for performance, reliability, and scalability, leveraging methodologies like LLMOps/AIOps. Innovation and Learning: Stay updated with the latest AI advancements and apply innovative techniques to solve complex challenges. Qualifications: Must have: Educational Background: Bachelor's degree in Computer Science, Engineering, or a related STEM field. Professional Experience: At least 3 years of experience in software engineering with a strong focus on Python, and 6+ months of hands-on work with frameworks like LangChain, OpenAI API, or similar. GenAI Knowledge: Practical experience in prompt engineering for Large Language Models (LLMs), including embeddings and vector databases. Software Engineering Practices: Solid understanding of best practices, including version control (e.g., Git), code reviews, and testing methodologies. Problem-Solving and Collaboration: Strong analytical skills with the ability to think agilely and connect concepts effectively.
Excellent communication, teamwork, and a proactive approach to challenges. Continuous Learning: Passion for staying up-to-date with advancements in AI and software development. Nice to have: Advanced AI Expertise: Experience with machine learning and deep learning frameworks (e.g., PyTorch, Hugging Face), fine-tuning LLMs, and developing AI agents. Knowledge of NLP techniques: Knowledge of classical machine learning algorithms (e.g., decision trees, logistic regression). Model Evaluation: Proficiency in assessing machine learning models using performance metrics, validation techniques, and optimization strategies.
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