Be a hands-on problem solver with a consultative approach, applying Machine Learning & Deep Learning algorithms to solve business challenges.
Use knowledge of a wide variety of AI/ML techniques and algorithms to determine which combinations best solve the problem.
Improve model accuracy to deliver greater business impact.
Estimate business impact due to deployment of models.
Work with domain/customer teams to understand business context, data dictionaries, and apply relevant Deep Learning solutions for given business challenges.
Work with tools and scripts for sufficient data pre-processing & feature engineering for model development – Python / R / SQL / Cloud data pipelines.
Design, develop & deploy Deep Learning models using TensorFlow / PyTorch.
Experience in using Deep Learning models with text, speech, image, and video data.
Design & develop NLP models for:
Text Classification
Custom Entity Recognition
Relationship Extraction
Text Summarization
Topic Modeling
Reasoning over Knowledge Graphs
Semantic Search
Using NLP tools like Spacy and open-source TensorFlow, PyTorch, etc.
Design and develop Image Recognition & Video Analysis models using Deep Learning algorithms and open-source tools like OpenCV.
Knowledge of state-of-the-art Deep Learning algorithms.
Optimize and tune Deep Learning models for best possible accuracy.
Use visualization tools/modules to explore and analyze outcomes & for model validation, e.g., Power BI / Tableau.
Work with application teams to deploy models on cloud as a service or on-premises.
Deployment of models in Test / Control framework for tracking.
Build CI/CD pipelines for ML model deployment.
Integrate AI & ML models with other applications using REST APIs and other connector technologies.
Constantly upskill and update with the latest techniques and best practices. Write white papers and create demonstrable assets to summarize AI/ML work and its impact.

Technology / Subject Matter Expertise

Sufficient expertise in Machine Learning, mathematical, and statistical sciences.
Use of versioning & collaborative tools like Git / GitHub.
Good understanding of the landscape of AI solutions – cloud, GPU-based compute, data security and privacy, API gateways, microservices-based architecture, big data ingestion, storage, and processing, CUDA Programming.
Develop prototype-level ideas into solutions that can scale to industrial-grade strength.
Ability to quantify & estimate the impact of ML models.

Soft Skills Profile

Curiosity to think in fresh and unique ways with the intent of breaking new ground.
Ability to share, explain, and sell thoughts, processes, ideas, and opinions, even outside one's own span of control.
Ability to think ahead and anticipate needs for problem-solving.
Ability to communicate key messages effectively and articulate strong opinions in large forums.

Desirable Experience

Keen contributor to open-source communities and communities like Kaggle.
Ability to process huge amounts of data using PySpark / Hadoop.
Development & application of Reinforcement Learning.
Knowledge of Optimization / Genetic Algorithms.
Operationalizing Deep Learning models for customers and understanding nuances of scaling such models in real scenarios.
Optimize and tune Deep Learning models for best possible accuracy.
Understanding of stream data processing, RPA, edge computing, AR/VR, etc.
Appreciation of digital ethics and data privacy.
Experience working with AI & Cognitive Services platforms like Azure ML, IBM Watson, AWS SageMaker, Google Cloud.
Experience in platforms like DataRobot, CognitiveScale, H2O.AI.

AIML Data Scientist

.Net Full Stack developer + Blazor

Data Engineer AWS/ Power platform Engineer

Machine Learning Systems Integration & Test Engineer

Data Scientist

Golang Engineer – AI & Service Development