
AI/ML Engineer
1 week ago
Key Responsibilities:
- Design, develop, and fine-tune
Large Language Models (LLMs)
such as
GPT, BERT, LLaMA, Falcon, and similar architectures
for advanced NLP applications. - Build and enhance
Generative AI solutions
for chatbots, summarization, text generation, recommendation systems, and creative content generation. - Implement and optimize NLP tasks including
text classification, sentiment analysis, named entity recognition (NER), and information retrieval
. - Apply
transfer learning, prompt engineering, and fine-tuning techniques
to improve LLM performance for domain-specific use cases. - Research, prototype, and productionize state-of-the-art
AI/ML models
using
TensorFlow, PyTorch, Hugging Face Transformers
. - Develop and optimize
computer vision models
(CNNs, object detection, image recognition, OCR, image segmentation) for real-world applications. - Integrate and deploy AI models into production systems using
DevOps and MLOps pipelines
(CI/CD, Docker, Kubernetes, MLflow, Airflow). - Deploy, monitor, and scale AI solutions on cloud platforms (
AWS, GCP, Azure
). - Ensure models meet standards for
accuracy, performance, scalability, and security
. - Collaborate with data engineers, product managers, and researchers to translate business problems into AI-driven solutions.
- Stay updated with the
latest advancements in NLP, LLMs, multimodal AI, and computer vision research
, and integrate relevant findings into projects.
Requirements:
- 4+ years of hands-on experience in NLP and Large Language Models (LLMs)
(GPT, BERT, LLaMA, Falcon, Hugging Face ecosystem). - Proven expertise in
Generative AI applications
such as conversational AI, text summarization, and creative content generation. - 1+ year of experience with Computer Vision
, including
CNNs, object detection, image recognition, image segmentation, and OCR
. - Strong programming skills in
Python
and experience with
ML/DL frameworks
: TensorFlow, PyTorch, Hugging Face. - Experience building and deploying models in
production environments
using
DevOps and MLOps practices
(CI/CD pipelines, Docker, Kubernetes, MLflow, Airflow). - Proficiency with
cloud AI services
(AWS SageMaker, GCP Vertex AI, Azure ML). - Solid understanding of
data preprocessing, feature engineering, and large-scale data pipelines
. - Familiarity with
vector databases (Pinecone, Weaviate, FAISS)
for LLM-powered search and retrieval. - Strong knowledge of
transformer architectures, embeddings, attention mechanisms, and prompt optimization
. - Bachelor's or Master's degree in
Computer Science, Artificial Intelligence, Machine Learning, or a related field
.
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