Senior AI/ML Engineer
1 week ago
Principal Responsibilities:
- Develop and maintain scalable, secure AI and machine learning applications utilizing Python, machine learning frameworks (e.g., TensorFlow, PyTorch), and cloud services
- Design and implement machine learning models and algorithms to support various AI-driven client applications, with a focus on user interface interactions and AI-driven features.
- Integrate third-party AI/ML APIs and services to existing web applications.
- Promote a data-driven and machine learning approach with a commitment to delivering valuable AI enhancements consistently.
- Deep understanding of LLMs (open source). We focus on a wide variety of NLP use cases including writing assistance, summarization, and concept extraction.
- Lead and participate in NLP and computer vision model development, providing constructive feedback to foster a culture of continuous improvement among team members.
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Requirements:
- 3-6 years of experience as an active coder, with proficiency in Python 3.x, strong Object-Oriented Programming (OOP) skills, and familiarity with modern Python features.
- Proven experience in Natural Language Processing (NLP) and Computer Vision (CV).
- In-depth knowledge of essential Python libraries such as numpy, pandas, scikit-learn, TensorFlow, PyTorch, Keras, Transformers, and others relevant to machine learning.
- Competence in working with cloud environments (AWS, Azure, GCP, Databricks) and Linux, including Lambda/Serverless, SQS, SNS, S3, and EC2.
- Experience deploying Transformer-based models into production.
- Proficiency in Django or Flask is a huge plus.
- Strong expertise in source control, code review, and repository management using Git.
- Familiarity with software engineering principles and design patterns, including Dependency Injection, SOLID, Service Containers, and Providers.
- Experience with containerization technologies like Docker.
- Proficiency in building highly distributed, eventually consistent AI systems.
- Familiarity with microservices architecture and message broker systems.
- Expertise in various machine learning testing methodologies, including unit testing, integration testing, performance testing, and load testing.
- Knowledge of data visualization, monitoring, and alerting concepts along with relevant tooling.
- Excellent knowledge of Relational Databases, SQL, and ORM technologies such as SQLAlchemy.
- Knowledge of LLMs, including fine-tuning and deployment integration with web applications.
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