Senior Software Engineer - AI/ML Engineer AWS

Position Description:

Your future duties and responsibilities:

• We are seeking an experienced Senior AI/ML Engineer to join our advanced AI engineering team focused on building next-generation intelligent agent solutions and enterprise AI applications. The ideal candidate will have strong expertise in designing, developing, and deploying scalable AI/ML and agent-based systems using AWS cloud-native technologies and modern LLM frameworks. In this role, you will lead the development of autonomous and multi-agent architectures leveraging AWS Bedrock, LangChain, Graph Databases, RAG pipelines, and orchestration frameworks to solve complex enterprise business problems. You will work closely with cross-functional teams to design secure, scalable, and production-grade AI solutions while ensuring performance optimization, governance, and responsible AI compliance. The candidate should possess deep technical expertise in AI application development, prompt engineering, serverless architectures, API integrations, and enterprise-grade deployment practices. Experience with AWS AI ecosystem tools, vector databases, containerized deployments, and modern AI development methodologies is highly desirable. This role requires strong analytical thinking, hands-on engineering capability, and the ability to work in a fast-paced Agile environment delivering innovative AI-powered business solutions. .. Responsibilities: Serve as a Senior AI/ML Engineer for agent-based initiatives, including scoping, estimation, and architectural design of AI & Business Applications. . Design, develop, and deploy intelligent agents using AWS Bedrock, LangChain, Kiro, Graph DB, Strands, and related agent frameworks. . Build and maintain applications using AWS agent core framework, orchestration layers, and hierarchical agent workflows to support complex reasoning and automation use cases. . Develop secure, scalable APIs using AWS Lambda and API Gateway to expose AI and agent capabilities to downstream systems. . Implement prompt engineering best practices to optimize model performance, accuracy, and reliability across use cases. . Design and implement RAG (Retrieval Augmented Generation) pipelines leveraging vector databases and knowledge graphs. . Monitor, evaluate, and optimize LLM performance, cost, latency, and reliability in production environments. . Support testing cycles by validating AI outputs, identifying gaps, and improving agent behavior through tuning and iteration. . Ensure solutions meet enterprise standards for security, compliance, and responsible AI usage. Strong experience with Python & Spark for building AI, data, and agent-based applications. . Hands-on experience with AWS Bedrock and large language models (LLMs). . Experience using LangChain, Strands, or similar agent frameworks. . Strong understanding of agent core design, multi-agent systems, and orchestration patterns. . Experience building serverless solutions using AWS Lambda and API Gateway. . Solid knowledge of prompt & context engineering techniques and LLM optimization strategies. . Experience integrating LLMs with enterprise systems via APIs. . Build Solutions to any complex business problem using AI-Development Life Cycle methodology. . Strong problem-solving, communication, and collaboration skills.