AIML Engineer

Key Responsibilities:

Design, develop, and deploy conversational AI systems (chatbots, virtual assistants) that leverage LLMs to enable seamless voice interactions for banking services & Code generation and Completion including test cases.

Develop multi modal solution architecture with Agents with bespoke models for specific use-case/scenario

Fine-tune and optimize LLM models for specific banking tasks and user experiences.

Implement Retrieval Augmented Generation (RAG), and embeddings to retrieve customer information through APIs

Continuously monitor and evaluate the performance of voice-based banking solutions.

Develop and deploy machine learning models for tasks like personalized recommendations, and customer sentiment analysis.

Collaborate to deploy and manage models on GCP, leveraging its MLOps tools and services to streamline model development, training, and deployment.

Develop an end-to-end architecture from training to inferencing custom models

Skills:

Strong understanding of natural language processing (NLP) concepts and techniques, good programming skills

Knowledge on AI/ML algorithms building NLP Applications

Deep knowledge of LLM architectures and their applications.

Expertise in data preprocessing, feature engineering, and model evaluation.

Basic understanding of MLOps principles and practices.

Excellent problem-solving and communication skills.

Experience

3+ years of hands-on experience in AI/ML development, with a strong focus on NLP & API Integrations

Experience with LLM models, and fine-tuning, prompt engineering, and RAG techniques.

Proficiency in Python is Must

Knowledge on vector databases will be good

HandOn experience with frameworks TensorFlow, Pytorch, LangChain, FastAPIs, NoSQL Database

Knowledge on GCP/MLOPs will

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