Data Science & Engineering Lead

As the Data Science & Engineering Lead at our company, you will play a crucial role in driving AI and data innovation. You will have the opportunity to work closely with our talented team to create and deploy impactful ML models, enhance our data systems, and generate actionable insights. If you possess expertise in advanced AI tools such as Databricks, Spark, and Azure, and are passionate about exploring the boundaries of machine learning and engineering, we are excited to welcome you to our team. Join us for a rewarding work experience, a collaborative environment, and competitive benefits. **Key Responsibilities:** - Design and implement supervised and unsupervised ML models (e.g., OLS, Logistic Regression, Ensemble Methods) to address real-world business challenges. - Lead the development of advanced neural network architectures such as ANN, CNN, RNN, GAN, Transformers, and RESNet. - Drive progress in NLP using tools like NLTK and neural-based language models. - Oversee the creation of computer vision models with the utilization of OpenCV for practical applications. - Lead time-series modeling projects focusing on forecasting and anomaly detection. - Implement AI techniques like Retrieval-Augmented Generation (RAG), Chain of Thought (CoT), and Model of Alignment (MOA) to enhance model performance. - Develop and maintain scalable data pipelines for both streaming and batch processing. - Architect and optimize lakehouse solutions utilizing Delta/Iceberg and bronze-silver-gold architectures. - Lead the development of ETL processes supported by tools like Airflow, DBT, and Airbyte to facilitate data flow and transformation. - Design and optimize database models for OLTP and OLAP systems using Snowflake, SQL Server, PostgreSQL, and MySQL. - Build NoSQL solutions by leveraging MongoDB, DynamoDB, and ElasticSearch for unstructured data. - Lead efforts in constructing cloud infrastructure, particularly in AWS (preferred) or Azure, using services like Lambda, API Gateway, Batch processing, Kinesis, and Kafka. - Oversee MLOps pipelines for robust deployment of ML models in production with platforms like Sagemaker, Databricks, and Azure ML Studio. - Develop and optimize business intelligence dashboards with tools like Tableau, QuickSight, and PowerBI for actionable insights. - Implement GPU acceleration and CUDA for model training and optimization. - Mentor junior team members in cutting-edge AI/ML techniques and best practices. **Qualifications Required:** - Proven expertise in both supervised and unsupervised ML, advanced deep learning, including TensorFlow, PyTorch, and neural network architectures (e.g., CNN, GAN, Transformers). - Hands-on experience with machine learning libraries and tools such as Scikit-learn, Pandas, and Numpy. - Proficiency in AI model development using LLM libraries (e.g., Langchain, Huggingface, OpenAI). - Strong MLOps skills, with experience deploying scalable pipelines in production using tools like Sagemaker, Databricks, and Azure ML Studio. - Advanced skills in big data frameworks like Apache Spark, Glue, and EMR for distributed model training. - Expertise in ETL processes and data pipeline development with tools like Airflow, DBT, and Airbyte. - Strong knowledge in lakehouse architectures (Delta/Iceberg) and experience with data quality frameworks like Great Expectations. - Proficiency in cloud platforms (AWS preferred or Azure), with a deep understanding of services like IAM, VPC networking, Lambda, API Gateway, Batch, Kinesis, and Kafka. - Proficiency with Infrastructure as Code (IaC) tools such as Terraform or CloudFormation for automation. - Advanced skills in GPU acceleration, CUDA, and distributed model training. - Demonstrated ability to architect and deploy scalable machine learning and data-intensive systems. - Proficiency in database modeling for OLTP/OLAP systems and expertise with relational and NoSQL databases. - Strong mentoring skills, with a proven track record of guiding junior team members in AI/ML best practices. - Bachelor's degree in a related field (required), with a Master's or PhD in Data Science or a related field (preferred). - 7+ years of experience in data science, machine learning, or data engineering. - AWS or Azure certification (strongly preferred). - Strong communication and leadership skills with experience working cross-functionally to deliver high-impact data solutions. Particle41 is dedicated to its core values of Empowering, Leadership, Innovation, Teamwork, and Excellence. We are committed to creating a diverse and inclusive work environment where individuals from all backgrounds can thrive. If you are aligned with our values and mission, we encourage you to apply and be a part of our team. Particle41 provides equal employment opportunities based on merit and qualifications without discrimination. If you require any assistance during the application process, feel free to reach out to us at careers@Particle41.com. As the Data Science & Engin