Software Engineer (AI)

Software Engineer with a focus on Artificial Intelligence (AI) Engineering, including expertise in Large Language Models (LLMs), you will be instrumental in developing, deploying, and scaling AI-powered solutions, leveraging the power of LLMs and other advanced AI techniques, to address business challenges. You will work closely with data scientists, machine learning engineers, and product managers throughout the entire AI lifecycle, from research and experimentation to production implementation and monitoring of both traditional ML models and cutting-edge LLM applications. This role offers an exciting opportunity to contribute to innovative AI projects and make a significant impact on our products and services.

Responsibilities:

• Develop and implement scalable and robust AI/ML solutions, including those powered by LLMs: Translate machine learning models and algorithms, including large language models, developed by data scientists into production-ready software applications and services.
• Build and maintain AI infrastructure and pipelines, optimized for LLMs: Design, develop, and manage the infrastructure required for data ingestion, preprocessing, feature engineering (including prompt engineering data), model training (including fine-tuning of LLMs), deployment, and monitoring of both traditional ML and LLM-based systems.
• Optimize AI model performance and efficiency, with a focus on the unique challenges of LLMs: Work on improving the speed, scalability, and resource utilization of deployed AI models, including techniques for efficient LLM inference.
• Integrate AI models, including LLMs, with existing software systems: Develop APIs and integrate AI functionalities, including those driven by LLMs, seamlessly into our existing products and services.
• Implement and manage MLOps practices for both traditional ML and LLM workflows: Establish and maintain CI/CD pipelines for machine learning models, ensuring automated testing, deployment, and rollback capabilities, specifically considering the nuances of LLM deployment.
• Monitor and troubleshoot AI systems in production, including LLM-based applications: Proactively identify and resolve issues related to model performance, data quality, infrastructure, and the specific challenges of LLM stability and behavior.
• Collaborate with data scientists and machine learning engineers on both traditional models and LLMs: Participate in the model development process, providing engineering insights and ensuring feasibility for deployment, with a strong focus on the practical considerations of deploying and scaling LLMs.
• Stay up-to-date with the latest advancements in AI/ML, particularly in LLMs and generative AI, and software engineering: Continuously learn and evaluate new technologies and methodologies to improve our AI capabilities, with a keen eye on the rapidly evolving LLM landscape.
• Contribute to technical documentation, specifically addressing the architecture and deployment of LLM-powered features: Create clear and concise documentation for developed AI systems and processes, with specific attention to the intricacies of LLM integration.
• Ensure code quality and maintainability: Adhere to best practices for software development, including code reviews and testing, with considerations for the unique aspects of LLM-related code.

Requirements:

• Bachelor's in Computer Science, Software Engineering, or a related technical field.
• 3 years of experience as a Software Engineer with a demonstrable focus on AI/ML projects.
• Strong proficiency in Python and experience with relevant AI/ML libraries and frameworks such as TensorFlow, PyTorch, scikit-learn, Pandas, and NumPy.
• Experience in deploying and scaling machine learning models, including Large Language Models, in a production environment.
• Solid understanding of cloud computing platforms (e.g., AWS, Azure, GCP) and their AI/ML services, including services relevant to LLM deployment (e.g., managed inference endpoints).
• Hands-on experience with containerization technologies (e.g., Docker, Kubernetes).
• Familiarity with CI/CD pipelines and MLOps principles, with specific understanding of how they apply to LLMs.
• Experience with API development and integration, including building APIs for interacting with LLMs.
• Strong understanding of software development principles, data structures, and algorithms.
• Excellent problem-solving, analytical, and debugging skills, including the ability to troubleshoot issues specific to LLM behavior.
• Strong communication and collaboration skills, with the ability to work effectively in a team environment, including discussing the nuances of LLM capabilities and limitations.
• Have experience working with Large Language Models (LLMs) and Transformer architectures (e.g., GPT, BERT, Llama, DeepSeek). This includes practical experience in prompt engineering, fine-tuning, evaluation, and deployment of LLMs.

Preferred Qualifications:

• Experience with specific AI domains such as Natural Language Processing (NLP), Computer Vision, or Recommendation Systems, with a good exposure on how LLMs can be applied within these domains.
• Experience in deploying and optimizing LLMs for low-latency inference and high throughput.
• Familiarity with big data technologies (e.g., Spark, Hadoop), especially in the context of processing large text datasets for LLM training or application.
• Experience with monitoring and logging tools (e.g., Prometheus, Grafana, ELK stack) for tracking the performance and behavior of LLM applications.
• Contributions to open-source AI/ML or LLM-related projects.
• Experience working in an agile development environment.