Senior Software Engineer - Python/AI/ML

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 LLM's(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

Qualification

Bachelor's degree in Computer Science or a related field.
Proven experience as a Machine Learning Engineer with at least 4 years of experience.
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).

Required Skills

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..