Generative AI Engineer

We are seeking an GenAI Machine Learning Engineer for one of our clients, and this is a fully remote position.

Key Responsibilities:

• Building and integrating generative AI applications for customer interactions by using LLMs and orchestration frameworks.
• Driving the design, development and scaling of internal AI agents and customer-facing agentic solutions such as GenAI-powered Contact Centers
• Designing and deploying custom solutions on AWS Bedrock by using foundation models from leading providers.
• Fine-tuning and evaluating large language models by using proprietary and external datasets.
• Building scalable APIs and backend services to support real-time AI inference.
• Ensuring reliability, privacy, and accuracy of Gen AI responses by applying testing and monitoring tools.
• Contributing to governance efforts by ensuring generative AI solutions follow responsible AI principles, including transparency, data privacy, and compliance with industry standards.
• Working with the data science team to apply generative AI to other business areas, including document processing, claims decisioning, and reporting.
• Supporting global GenAI roadmap bringing expert insights on the technology to adopt
• Documenting solutions and collaborating with engineering, product, and customer teams to align requirements and output.
• Tracking industry trends and recommending tools or approaches to improve system performance and capability.

Required Skills & Experience:

• 6+ years of experience in AI and machine learning, out of which 3+ years of experience in machine learning engineering, NLP, Generative AI, and LLM technologies.
• Experience with LLM Agentic workflows and frameworks (Langchain, LangGraph, LlamaIndex, etc.).
• Knowledge and experience with transformer architectures, prompt engineering, retrieval-augmented generation (RAG), guardrails, and LLM evaluation methodologies.
• Hands-on experience of MLOps and LLMOps is desired.
• Knowledge of design, implementation and management of distributed training pipelines for LLMs to ensure scalability and efficiency are nice to have.
• Advanced knowledge of Python and some experience in ML frameworks (PyTorch, scikit-learn, etc.) for training, fine-tuning, and deploying generative models is desired.
• Experience with Cloud technology, preferably AWS.
• Experience with Voice Conversational AI will be a big plus.