AI Engineer (Mid-Level)

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.
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.