Generative AI Engineer

Responsibilities

• Design, prototype, and deploy Generative AI models (LLMs, Transformers, Diffusion models) for real-world enterprise use cases. Build and fine-tune LLM-based applications such as: Chatbots, Document Q&A systems, Report generators, Code assistants and Summarization tools. Apply prompt engineering, Retrieval-Augmented Generation (RAG), and context-aware pipelines to enhance model accuracy and relevance. Integrate AI models with enterprise systems, APIs, and data stores using Python, Java, or Node.js. Collaborate with architects to define scalable, secure, and cost-efficient AI service architectures. Implement AI/ML pipelines for training, validation, and deployment using tools like MLflow, Vertex AI, or Azure ML. Monitor model performance, detect drift, and drive continuous improvement. Optimize inference performance and cost through model compression, quantization, and API optimization. Ensure compliance with AI ethics, security, and governance standards. Prepare and curate training datasets (structured/unstructured text, images, code). Apply data preprocessing, tokenization, and embedding generation techniques. Work with vector databases (e.g., Pinecone, Weaviate, FAISS, Chroma) for semantic search and retrieval. Partner with business stakeholders to identify and shape impactful AI use cases. Contribute to the development of a strategic AI adoption roadmap and reusable AI Workbench/platform components. Support POCs, pilots, and full-scale implementations using agile methodologies. Document and present solution designs, technical findings, and outcomes to leadership and clients. SKILLS Must have
• Overall 7+ years of experience and relevant 4+ years of GenAI. Strong programming skills in Python (preferred), with experience in Java or Node.js. Hands-on experience with LLMs (e.g., GPT, LLaMA, Claude, Mistral), Transformers, and Diffusion models. Experience with Hugging Face Transformers, LangChain, LLM orchestration frameworks, and prompt tuning. Familiarity with RAG pipelines, embedding models, and vector databases. Experience with cloud platforms (AWS, GCP, Azure) and AI/ML services. Knowledge of MLOps tools and practices (e.g., MLflow, Kubeflow, Vertex AI, Azure ML). Strong understanding of data engineering, data pipelines, and ETL workflows. Excellent problem-solving, communication, and stakeholder engagement skills. Bachelor's or Master's degree in Computer Science, AI/ML, Data Science, or related field.