We are seeking a
talented and motivated AI Engineer
with expertise in
Large Language Models (LLMs)
,
Natural Language Processing (NLP)
, and
Speech-to-Text
technologies.
As part of our dynamic team, you will design, develop, and deploy next-generation AI solutions that enhance our products and services through intelligent automation, language understanding, and seamless communication systems.
Key Responsibilities
LLM Development & Integration
-
Fine-tune and deploy Large Language Models for chatbots, content generation, and virtual assistants
-
Evaluate and optimize model performance in real-world use cases
-
Integrate LLMs into production environments ensuring scalability and reliability
NLP System Design
-
Design and implement NLP algorithms for text classification, sentiment analysis, entity recognition, and summarization
-
Handle large text datasets for model training and validation
-
Collaborate with cross-functional teams to address language-related challenges
Speech-to-Text Implementation
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Develop and optimize speech-to-text (ASR) pipelines for multiple languages and dialects
-
Integrate speech recognition systems with NLP and LLM modules for end-to-end conversational experiences
-
Stay updated with advancements in Automatic Speech Recognition (ASR) technologies
Performance Optimization
-
Improve AI model efficiency for real-time performance and scalability
-
Identify and mitigate biases to ensure model accuracy, fairness, and robustness
Research & Innovation
-
Stay current with cutting-edge research in LLMs, NLP, and Speech AI
-
Experiment with new architectures and techniques to drive innovation
Documentation & Collaboration
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Maintain detailed documentation of models, datasets, and workflows
-
Collaborate with product managers, software engineers, and other stakeholders to deliver production-grade AI solutions
Requirements
Qualifications
-
Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or related fields
-
Proven experience in LLM development (e.g., OpenAI GPT, Claude, or similar frameworks)
-
Strong knowledge of NLP libraries (e.g., Hugging Face, spaCy, NLTK)
-
Hands-on experience with speech-to-text tools (e.g., Whisper, Google Speech API, DeepSpeech)
-
Proficiency in Python and frameworks like TensorFlow or PyTorch
-
Excellent analytical, problem-solving, and communication skills