Engineer (Gen AI)

• 2- 5 years of experience in building and deploying Generative AI and Deep Learning models in production environments.
• Strong understanding of Neural Network fundamentals, including model architectures, optimization techniques, and performance evaluation.
• In-depth knowledge of Large Language Model (LLM) architectures, training methodologies, and fine-tuning techniques such as LoRA and QLoRA.
• Practical experience in Natural Language Processing (NLP) and embedding techniques, including Word2Vec, GloVe, and Sentence Transformers.
• Hands-on experience designing and implementing Retrieval-Augmented Generation (RAG) pipelines and working with vector databases such as Pinecone and ChromaDB.
• Strong proficiency in Python and core data science libraries/frameworks, including TensorFlow, PyTorch, Hugging Face, and LangChain.
• Experience with MLOps practices and tools such as Docker, Kubernetes, and MLflow for model deployment, monitoring, and lifecycle management.
• Familiarity with cloud platforms including AWS, GCP, and Azure for scalable AI solution deployment.
• Experience integrating APIs from major AI providers such as OpenAI and Google.
• Excellent problem-solving skills with the ability to work both independently and collaboratively in cross-functional teams.
• Contributions to open-source AI/ML projects or a strong project portfolio demonstrating practical expertise.
• Published research in AI, NLP, or Machine Learning is a plus.

Job responsibilities

• Design, develop, and deploy advanced Generative AI solutions using state-of-the-art models and frameworks.
• Train, fine-tune, and optimize Large Language Models (LLMs) for domain-specific use cases.
• Implement and enhance Retrieval-Augmented Generation (RAG) pipelines with vector databases.
• Build robust Deep Learning and Neural Network architectures for scalable AI systems.
• Apply NLP techniques including text classification, sentiment analysis, NER, and embedding generation.
• Manage the end-to-end AI lifecycle: data preprocessing, embedding preparation, model training, evaluation, deployment, and monitoring.
• Develop clean, efficient, and well-documented Python code.
• Collaborate with cross-functional teams (Product, Engineering, Data Science) to deliver high-impact AI capabilities.
  • Stay current with the latest AI advancements, tools, and research.