AI Engineer

Responsibilities

• Clean, organize, and preprocess large, complex datasets, identify relevant data sources, and manage data quality to support AI capabilities within our products.
• Design, develop, and optimize the architecture, implement scalable LLM-based services and appropriate ML algorithms and tools, and continuously improve model performance and efficiency.
• Develop components, services, and modules for data classification, information extraction, and interpretation, and build optimized data pipelines.
• Integrate and monitor LLM-based services into products with attention to latency, costs, security, safety, and reliability. Implement automated testing and gather feedback to improve prompting and results.
• Use statistical analysis to interpret test results and understand model performance, particularly its problem-solving capabilities.
• Collaborate with AI Engineers, software engineers, and product managers to align AI/ML solutions with business objectives and optimally communicate technical concepts and project updates to non-technical stakeholders.
• Stay updated on the latest AI/ML developments, research, and tools, and evaluate emerging methodologies to implement innovative solutions.
• Advocate for security, privacy, and ethics in AI/ML outputs, and participate in code reviews to ensure software engineering best practices.

Qualifications

• Bachelor's degree in Data Science, Mathematics, Computer Science, or a related field.
• 3+ years of experience with ML frameworks such as PyTorch, Dask, Spark, or TensorFlow.
• Proficiency in programming with R, Python, SQL, or similar languages.
• Experience with frameworks LangChain, LangFuse, LangGraph or similar.
• Experience with LLM lifecycle management such as versioning, monitoring, and feedback loops.
• Experience with Large Language Models (LLMs), Big Data, and NoSQL databases and building systems that utilize LLMs for AI
• Experience using AI services from AWS, OpenAI, etc.
• Strong knowledge of Deep Learning concepts.
• Understanding of probability, statistics, and machine learning concepts such as precision, recall, optimization, hyperparameter tuning, overfitting, and interpretability.
• Shown ability to apply standard implementations of machine learning algorithms effectively.
• Understanding of system design to avoid bottlenecks and ensure scalability with increasing data volumes.
• Self-motivated and able to work independently in a dynamic environment.

Application Process

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