Technical Lead
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
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
Maintain detailed documentation of models, datasets, and workflows.
Collaborate with product managers, software engineers, and other stakeholders to
deliver production-grade AI solutions.
Requirements
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.