AI Solutions Software Developer
As an AI Solutions Software Developer, you will be responsible for designing, developing, testing, and deploying high-performance software systems that integrate advanced artificial intelligence and machine learning technologies. You will collaborate closely with cross-functional teams including data scientists, AI researchers, product managers, and UX designers to translate business requirements into scalable, reliable AI-driven solutions. Your work will directly contribute to the development of innovative products that solve complex problems across various domains (e.g., healthcare, finance, e-commerce, enterprise services).
Key Responsibilities:
Design and implement software architectures for AI solutions, including data pipelines, model integration, inference engines, and user-facing interfaces.
Develop and optimize code for machine learning models (e.g., training, validation, deployment) using programming languages and frameworks relevant to AI development.
Collaborate with data scientists to integrate pre-trained or custom ML models into production-ready software systems, ensuring compatibility, efficiency, and scalability.
Build and maintain data processing pipelines to handle large volumes of structured and unstructured data (e.g., data collection, cleaning, transformation, storage) that fuel AI models.
Conduct unit testing, integration testing, and performance testing to ensure the reliability, robustness, and efficiency of AI software solutions.
Deploy AI solutions to cloud platforms (e.g., AWS, Azure, GCP) or on-premises environments, implementing CI/CD pipelines for seamless updates and maintenance.
Troubleshoot and resolve technical issues in AI software systems, including model performance bottlenecks, data quality problems, and integration errors.
Stay updated with the latest advancements in AI/ML technologies, software development practices, and industry trends to continuously improve solution performance and functionality.
Document software designs, development processes, deployment procedures, and user guides to ensure knowledge sharing and operational clarity.
Collaborate with product managers to align software development with business goals, prioritize features, and deliver solutions within project timelines.
Qualifications
Required Qualifications:
Bachelor’s degree in Computer Science, Software Engineering, Electrical Engineering, or a related technical field (Master’s degree preferred).
3+ years of hands-on software development experience, with at least 1-2 years focused on AI/ML solution development.
Proficiency in programming languages commonly used in AI development: Python (required), Java, C++, or JavaScript.
Strong experience with AI/ML frameworks and libraries
Knowledge of data processing and storage technologies: SQL (PostgreSQL, MySQL).
Familiarity with cloud computing platforms (AWS, Azure, GCP) and their AI/ML services (e.g., AWS SageMaker, Azure ML, GCP AI Platform).
Understanding of software development best practices: Agile/Scrum methodologies, version control (Git), CI/CD pipelines (Jenkins, GitHub Actions), and test-driven development (TDD).
Experience in designing and implementing scalable, high-performance software architectures for data-intensive or AI-driven applications.
Strong problem-solving skills, analytical thinking, and the ability to work independently and in cross-functional teams.
Excellent communication skills (verbal and written) to convey technical concepts to both technical and non-technical stakeholders.
Preferred Qualifications:
Experience with large language models (LLMs) such as GPT, BERT, or Llama, and their fine-tuning, deployment, and integration into applications.
Knowledge of MLOps practices, including model versioning, experiment tracking (MLflow, Weights & Biases), and model monitoring in production.
Familiarity with containerization and orchestration tools: Docker, Kubernetes.
Experience in a specific industry domain (e.g., healthcare, finance, e-commerce) with AI solution deployment.
Contributions to open-source AI/ML projects or a strong portfolio of AI software development projects.
Knowledge of edge AI deployment (deploying AI models on edge devices such as IoT sensors, mobile devices).
Understanding of data privacy and security regulations relevant to AI solutions handling sensitive data.