AI/ML Engineer For Gen AI Adoption

Senior AI/ML engineer for Gen AI adoption in Test Automation

This role is central to the development and deployment of intelligent AI systems that would enhance service value proposition, operational efficiency, and strategic decision-making. The individual will design, develop, train, and implement machine learning and deep learning models capable of processing large datasets and generating actionable insights. Collaboration with data scientists, software developers, and business leaders is essential to integrate these models into existing systems. The position requires a deep understanding of machine learning/NLP, deep learning, data processing, and generative AI to ensure that solutions are robust, scalable, and impactful.

Senior AI/ML engineer with exceptional proficiency in programming languages (Python, Java) and expertise in data processing tools (Pandas, NumPy). Possesses in-depth knowledge of cutting-edge AI/ML technologies, including Generative AI, RAG architecture, and related frameworks (Langchain, Llamaindex).

Data Science Mastery: Adept in data cleaning, feature engineering, and data augmentation techniques, with hands-on experience in end-to-end machine learning projects, from data collection to model deployment.

Financial Domain Expertise: Good understanding in financial applications and core banking operations, with familiarity in testing processes and methodologies specific to the industry.

Technical Versatility: Proficient in cloud platforms, AI/ML services, and Knowledge Graph databases. Skilled in creating and implementing automation and RPA frameworks.

Key Strengths:

- Advanced AI/ML expertise- Strong programming skills- Data science proficiency- Financial domain knowledge- Technical versatility

5-12 years of experience relevant to this position.

Strong AI/ML/NLP/Gen AI experience

BE/BTech/Masters

Responsibilities displayed in the job posting

1. Create and optimize models capable of generating high-quality content, such as text, images, audio, or video, based on specified parameters and datasets.
2. Design, build, and optimize machine learning models, deep learning architectures to solve specific business challenges.
3. Collect, clean, and preprocess large datasets to ensure high-quality data is used for training models.
4. Train AI models on large datasets, tune hyperparameters, and evaluate model performance using appropriate metrics.
5. Conduct experiments to test model accuracy, efficiency, and scalability.
6. Deploy AI models into production environments, ensuring they integrate smoothly with existing software systems. Monitor, update & refine the performance of deployed models.
7. Stay up to date with the latest advancements in AI, machine learning, and related technologies.
8. Communicate technical concepts, model results, and AI capabilities to non-technical stakeholders.
9. Ensure AI models and solutions adhere to ethical guidelines.
10. Enable and support fellow team members.