AI Engineer
barclays
Job Description
- Design & Implement Agentic, GenAI Solutions: Build robust Generative AI and Agentic AI applications using AWS Bedrock, SageMaker, and other AWS ML services.
- Develop LLM bases Use Cases: Architect and implement LLM based use cases like RAG, Summarization, data analysis etc. for enterprise-scale applications.
- LLM Finetuning & Evaluation: Fine-tune, evaluate, and optimize Large Language Models (LLMs) for performance, accuracy, and safety.
- Conversational AI Expertise: Design and deploy conversational AI models using Amazon Lex, Amazon Connect, and custom NLP pipelines.
- Integration & Collaboration: Work closely with product managers, engineers, and UX teams to embed AI capabilities into business workflows.
- Innovation & Research: Stay ahead of AI trends, frameworks, and best practices; apply them to drive continuous innovation.
- Quality & Governance: Maintain system design integrity, review test strategies, and ensure compliance with AI ethics and security standards.
- Incident Support: Assist in diagnosing and resolving production issues.
- Optimize solutions through thorough research experimentation, and advanced problem-solving techniques.
- Communicate complex concepts and results effectively to both technical and non-technical stakeholders.
- Strong proficiency in Python around Agentic, GenAI and AI/ML libraries (LangChain, LangGraph, Google ADK, Langfuse, OTEL,Transformers, CrewAI etc.).
- Proficiency in developing on AWS Solution including AIML ecosystem
- Deep understanding of AI Agents, Agentic architecture, Agentic Memory,Generative AI, LLMs, NLP, and Conversational AI.
- Deep understanding on Prompt engineering and Prompt management, refining and optimizing prompts to enhance the outcomes of Large Language Models (LLMs)