Agentic AI Solutions Developer
Pay Range: $60hr - $65hr
Responsibilities:
Design, develop, and deploy agentic AI solutions using deep agent frameworks such as LangChain and LangGraph.
Build multi-agent workflows leveraging LLMs (e.g., Claude via Amazon Bedrock) for reasoning, orchestration, and task execution.
Develop robust Python-based services and pipelines for Generative AI and AI-driven automation use cases.
Integrate AI agents with AWS data services including Glue, Athena, and S3 for data retrieval, processing, and analytics.
Implement Retrieval-Augmented Generation (RAG) and tool-enabled agent architectures.
Ensure solutions are scalable, secure, auditable, and production-ready.
Collaborate with data platform and product teams to translate business problems into agent-based AI architectures.
Contribute to architectural decisions, technical standards, and reusable frameworks for GenAI ecosystems.
Support model governance, prompt versioning, observability, and performance optimization.
Required Qualifications:
Strong proficiency in Python with experience building enterprise-grade applications.
Experience developing APIs, microservices, and backend services for AI workloads.
Solid understanding of object-oriented programming, asynchronous processing, and modular architectures.
Hands-on experience with LangChain, LangGraph, and deep agent frameworks.
Experience building autonomous agents, multi-agent systems, and stateful/event-driven workflows.
Strong knowledge of Generative AI concepts including prompt engineering, chain-of-thought reasoning, tool-augmented LLMs, and RAG patterns.
Practical experience working with Claude models via Amazon Bedrock.
Familiarity with hallucination mitigation, evaluation techniques, and response quality control.
Strong experience with AWS services including Amazon Bedrock, AWS Glue, Amazon Athena, and Amazon S3.
Understanding of cloud security, IAM, logging, and cost optimization for AI workloads.
Agentic AI Expertise:
Experience building AI agents from scratch, not just consuming APIs.
Ability to design agents with goal-driven reasoning, tool usage, memory, and state management.
Experience implementing multi-step task decomposition and agent orchestration.
Experience designing multi-agent collaboration systems.
Ability to integrate agents with APIs, databases, and enterprise data sources.
Familiarity with agent observability, failure handling, and explainability.
Ability to translate business workflows into agent-based execution models.
Preferred Skills:
Experience with scalable AI architecture and distributed systems.
Knowledge of monitoring, logging, and performance tuning for AI systems.
Strong problem-solving and analytical thinking skills.
Excellent communication and collaboration abilities.