Work closely with product managers to understand business requirements and translate them into technical solutions.
Collaborate with data scientists, data engineers, data analysts, software engineers, IT specialists, and stakeholders to expand effective use of AI applications.
Collaborate with cross-functional teams to design, develop, and maintain highly complex AI/ML systems.
Develop and implement AI/ML interfaces, services, and analytic applications to support the company's initiatives and projects.
Deploy machine learning models into production environments, ensuring scalability, reliability, and real-time performance. This may involve containerization, API development, and integration with existing systems.
Optimize machine learning algorithms and infrastructure for performance, scalability, and cost-efficiency. This may involve parallelization, distributed computing, and resource management.
Develop User Interfaces (UI) which support the business exploration and interaction with AI/ML models, scenarios, and planning.
Work will business applications teams to code AI/ML intensive software and algorithms.
Build services for driving intelligent decisions, interacting with machine learning models.
Conduct research and stay updated on the latest advancements in AI/ML technology and tools.
Analyze and optimize system performance to ensure efficient and effective use of resources.
Deliver features to production while considering functional and non-functional requirements, including security, latency, disaster recovery, and performance.
Take an active part in a Scrum team to deliver high quality software to the business.
SUPERVISORY RESPONSIBILITIES
This position may have direct reports depending on the project and organizational structure.
QUALIFICATIONS
Bachelor's degree in Computer Science, Computer Engineering, Information Technology or other relevant technical discipline
8+ years of experience in developing business applications for Machine Learning and Data Science workloads.
Strong programming skills in Python and Java; experience with Machine Learning libraries and frameworks.
Experience with common data science tools such as Python, R, PyTorch, TensorFlow, Keras, NLTK, Spacy, or Neo4j, and a good understanding of modeling platforms such as SageMaker, Databricks, and Dataiku.
Experience with data management technologies such as Databricks, Apache Spark, Hadoop, Kafka.
Experience developing and deploying Machine Learning solutions on cloud platforms (e.g., AWS, Azure, or GCP). AWS Preferred.
Experience containerizing analytical models using Docker and Kubernetes or other container orchestration platforms.
Technical expertise across all deployment models on public cloud, private cloud, and on-premises infrastructure.
Experience creating, documenting, and communicating software designs for complex products.
Skilled with domain-driven, event-driven, and microservice architectures.
Proficient in building, tracking, and communicating plans within agile processes.
Capable of coaching/mentoring individuals and teams.