Guide a team of software engineers in developing software products and solutions, providing guidance, code reviews, and technical support.
Collaborate with cross-functional teams to define project requirements, ensuring successful delivery of software projects.
Assist in the professional growth and mentorship of team members through regular feedback, coaching, and career development planning.
Contribute to the execution of the software engineering strategy, participating in strategic discussions and technology assessments.
Manage project tasks and assignments, prioritizing and planning work to meet project goals.
Monitor project timelines, resource allocation, and budgets to ensure efficient project execution.
Proactively identify and mitigate project risks, resolving issues as they arise.
Promote a culture of innovation, collaboration, and continuous learning within the team.
Develop and implement machine learning models and algorithms, including supervised, unsupervised, deep learning, and reinforcement learning techniques.
Implement generative AI solutions using technologies like RAG (Retrieval-Augmented Generation), Vector DBs, and frameworks such as LangChain and Hugging Face.
Utilize popular AI/ML frameworks and libraries such as TensorFlow, PyTorch, and Scikit-learn.
Design and deploy NLP models and techniques, including text classification, RNNs, CNNs, and Transformer-based models like BERT.
Ensure robust end-to-end AI/ML solutions, from data preprocessing and feature engineering to model deployment and monitoring.
Demonstrate strong programming skills in languages commonly used for data science and ML, particularly Python.
Leverage cloud platforms and services for AI/ML, especially AWS, with knowledge of AWS Sagemaker, Lambda, DynamoDB, S3, and other AWS resources.
Requirements & Skills:
Bachelor’s degree in a relevant field (e.g., Computer Science) or equivalent combination of education and experience.
Typically, 7+ years of relevant work experience in AI/ML/GenAI
10+ years of overall work experience. With a proven ability to manage projects and activities.
Proficiency in machine learning algorithms and techniques, including supervised and unsupervised learning, deep learning, and reinforcement learning.
Extensive experience with AI/ML frameworks and libraries such as TensorFlow, PyTorch, and Scikit-learn.
Strong knowledge of natural language processing (NLP) techniques and models, including Transformer-based models like BERT.
Experience with generative AI technologies, including RAG, Vector DBs, and frameworks such as LangChain and Hugging Face.
Proficient programming skills in Python and experience with cloud platforms like AWS.
Experience with AWS Cloud Resources, including AWS Sagemaker, Lambda, DynamoDB, S3, etc., is a plus.
Proven experience leading a team of data scientists or machine learning engineers on complex projects.
Strong project management skills, with the ability to prioritize tasks, allocate resources, and meet deadlines.
Excellent communication skills and the ability to convey complex technical concepts to diverse audiences.
Experience in setting technical direction and strategy for AI/ML projects.
Ability to mentor and coach junior team members, fostering growth and development.
Proven track record of successfully managing AI/ML projects from conception to deployment.
