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Job Description & How to Apply Below
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 Tensor Flow, 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 Lang Chain, Llama Index, or similar orchestration frameworks.
Exposure to computer vision or multimodal AI models.
Experience with containerization (Docker, Kubernetes) for ML workloads.
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