AI Engineer

Exponentia.ai is seeking a skilled AI Engineer with a specialization in Generative AI to join our dynamic team. In this role, you will lead the development and deployment of innovative generative AI solutions tailored specifically to the banking, financial services, and insurance (BFSI) domain. As part of our central digitization team, you will play a crucial role in driving the adoption of cutting-edge AI technologies to enhance operational efficiency and deliver superior customer experiences.

We are looking for a talented and motivated AI Engineer with 2 years of hands-on experience in Generative AI, specializing in Python, NLP (Natural Language Processing), and Audio/Visual Speech-to-Text solutions. The ideal candidate should have a solid understanding of Machine Learning (ML), Deep Learning (DL), and experience working with Large Language Models (LLMs), as well as speech processing technologies.

What does the job entail?

Developing, fine-tuning, and deploying Generative AI models for real-world applications.

Working on NLP tasks such as text summarization, language translation, sentiment analysis, and question answering.

Building and optimizing ML and DL pipelines for AI-based solutions.

Designing and implementing Audio/Visual Speech-to-Text systems for transcription and speech recognition applications.

Exploring and integrating advanced speech processing frameworks for tasks like speech synthesis and speaker identification.

Collaborating with cross-functional teams to integrate AI models into projects and applications.

Staying updated on the latest advancements in AI, speech technology, and applying them to enhance existing solutions.

Writing clean and efficient Python code for AI model development and deployment.

Documenting processes, experiments, and results for knowledge sharing.

2 years of hands-on experience in Generative AI, Python, NLP, Audio/Visual Speech-to-Text, and LLMs.

Proficiency in frameworks and tools like TensorFlow, PyTorch, Hugging Face, or similar.

Experience with audio processing libraries like Librosa, SpeechRecognition, or Kaldi is a plus.

Strong foundation in ML and DL concepts, including model training, evaluation, and optimization.

Hands-on experience with speech-related APIs like Google Speech-to-Text, AWS Transcribe, or similar.

Experience with cloud platforms (e.g., AWS, Azure, or GCP) for deploying AI solutions.

Familiarity with version control (e.g., Git) and collaboration tools.

Good problem-solving skills and a passion for working on cutting-edge AI and speech technologies.