Website:
ltm.com
Job details:
📋 Job Overview
We are looking for a skilled Gen AI + ML Engineer with strong expertise in Generative AI, Machine Learning, and Deep Learning. The ideal candidate will have hands-on experience building and deploying ML models, working with Large Language Models (LLMs), and developing AI-driven solutions using modern ML frameworks.
Applicants should have hands-on experience in Gen AI + Python and knowledge of Large Language Models (LLMs), RAG pipelines, embeddings, and prompt engineering. Practical experience with AI-driven and GenAI applications is preferred.
🔧 Key Responsibilities
- Design, develop, and deploy Machine Learning and Deep Learning models for production-grade applications.
- Build and fine-tune Large Language Models (LLMs) for domain-specific use cases.
- Develop and optimize RAG (Retrieval-Augmented Generation) pipelines, embeddings, and vector databases.
- Implement prompt engineering strategies to improve LLM output quality and accuracy.
- Collaborate with cross-functional teams to integrate Gen AI capabilities into existing products and platforms.
- Conduct model evaluation, A/B testing, and performance benchmarking for ML/AI solutions.
- Stay updated with the latest advancements in Gen AI, NLP, and ML research and apply them to real-world problems.
- Develop and maintain ML pipelines for data preprocessing, feature engineering, model training, and inference.
✅ Must-Have Skills
- Strong hands-on experience in Machine Learning, Deep Learning, and Generative AI.
- Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, Scikit-learn, and Hugging Face Transformers.
- Experience with Large Language Models (LLMs) — GPT, LLaMA, Mistral, or similar.
- Working knowledge of RAG pipelines, vector databases (Pinecone, Weaviate, FAISS), and embeddings.
- Solid understanding of prompt engineering, fine-tuning, and RLHF techniques.
- Experience with NLP tasks — text classification, NER, summarization, question answering, and sentiment analysis.
- Familiarity with cloud platforms (AWS, Azure, or GCP) for ML model deployment.
🌟 Good-to-Have Skills
- Experience with MLOps tools (MLflow, Kubeflow, or similar) for model lifecycle management.
- Knowledge of LangChain, LlamaIndex, or similar orchestration frameworks.
- Exposure to computer vision or multimodal AI models.
- Experience with containerization (Docker, Kubernetes) for ML workloads.
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