Responsibilities
- Apply machine learning and deep learning methods to address real-world business challenges with a consultative mindset
- Evaluate and combine various AI and ML algorithms to identify optimal solutions for specific problems
- Enhance model precision to increase measurable business value
- Quantify the expected business outcomes from model deployment
- Collaborate with domain experts and clients to grasp business requirements, interpret data definitions, and implement suitable deep learning approaches
- Utilize Python, R, SQL, or cloud-based data workflows to preprocess data and engineer features for modeling
- Build, train, and deploy deep learning models using frameworks such as TensorFlow and PyTorch
- Work with deep learning models on multimodal data including text, audio, images, and video
- Develop NLP systems for tasks like text classification, entity recognition, relationship detection, summarization, topic modeling, knowledge graph reasoning, and semantic search using tools like Spacy, TensorFlow, or PyTorch
- Create image and video analysis models using deep learning and libraries such as OpenCV
- Stay current with cutting-edge deep learning methodologies and architectures
- Fine-tune and optimize deep learning models to achieve peak accuracy
- Leverage visualization platforms like Power BI or Tableau to interpret results and validate models
- Partner with application teams to deploy models in cloud or on-premises environments
- Implement models within test and control setups for performance tracking
- Establish CI/CD pipelines to automate the deployment of machine learning models
- Integrate AI and ML models into enterprise applications via REST APIs and other integration technologies
- Continuously learn emerging techniques and best practices, while producing white papers and demonstrable artifacts showcasing impact
Interview Process
- Mode of Interview - In Person
- Date: 28th March 2026 (Saturday)
