Data Science / ML Engineer

 Data Science/ML Engineer

 We are seeking an experienced Data Science/ML Engineer with 6-8 years of expertise in AI/ML to design and deploy machine learning models. You will work with tools like TensorFlow, PyTorch, and AWS services such as Sagemaker. Experience in developing data pipelines, deploying models using MLOps, and building visualizations is required.

 Key Responsibilities:

•   Comprehend business issues: Propose valuable business solutions using statistical and AI/ML models.

•   Model Design: Design statistical models and machine learning/deep learning models to address complex business challenges.

•   Model Deployment: Develop and deploy ML/DL models into production environments.

•   Data Insights: Formulate available data sources, augment them, and extract valuable insights using SQL and efficient query writing techniques.

•   Data Visualization: Create innovative data visualizations and graphs using tools like d3js, dashplotly, and neo4j.

•   Integration: Ensure seamless integration of AI/ML models into business processes using cloud platforms like AWS or Azure.

•   Cross-Functional Collaboration: Work closely with cross-functional teams, translating business needs into actionable AI solutions.

•   Agile Participation: Participate in agile project delivery, ensuring the timely execution of deliverables.

 Required Technical Skills:

•   Python Programming: Proficient in Python programming.

•   Statistical Knowledge: Practical knowledge of Statistics and Operations Research methods.

•   Frameworks and Tools: Hands-on experience with frameworks and tools such as Flask, PySpark, PyTorch, TensorFlow, Keras, Databricks, OpenCV, Pillow/PIL, Streamlit, and neo4j.

•   Analytics/AI-ML Services: Expertise in Analytics/AI-ML AWS services, including SageMaker, Canvas, Bedrock.

•   Predictive Modeling: Strong understanding of predictive modeling techniques like regression models, XGBoost, random forests, GBM, Neural Nets, SVM, etc.

•   NLP Techniques: Proficient in NLP techniques like RNN, LSTM, and Attention-based models, with knowledge of Stanford NLP, IBM, Azure, and OpenAI NLP models.

•   MLOps: Experience in MLOps: deploying models into production on AWS or Azure cloud platforms.

•   Version Control: Working knowledge of version control tools such as GitHub and Bitbucket.

 Soft Skills:

•   Analytical Abilities: Strong analytical and problem-solving abilities.

•   Communication Skills: Excellent communication, listening, and probing skills.

•   Interpersonal Skills: Strong interpersonal skills, with the ability to collaborate and work effectively within a team.

 Good to Have:

•  AWS Specialty Certification in Data Analytics or Machine Learning.