Gen AI + ML Engineer

📋 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.

'05; 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.