Agentic AI Engineer – AWS Bedrock, LangGraph, RAG & Multi-Agent Systems

• Python: Expert proficiency for AI engineering, backend services, data pipelines, agent workflows, and production applications.
• Asynchronous backend APIs: Hands-on experience with FastAPI, Flask, or equivalent Python API frameworks.
• AWS Bedrock: Experience deploying and scaling foundation models, including model access, Knowledge Bases, Agents, Guardrails, and custom model evaluations.
• AWS services: Hands-on experience with EC2, Lambda, IAM, VPCs, and Step Functions.
• Agent orchestration frameworks: Deep experience with LangGraph, CrewAI, AutoGen, Semantic Kernel, or comparable frameworks.
• Multi-agent systems: Experience designing planners, executors, retrievers, validators, human-in-the-loop workflows, and tool-calling loops.
• RAG architectures: Experience designing advanced Retrieval-Augmented Generation systems, including ingestion, chunking, embeddings, metadata tagging, retrieval, reranking, and evaluation.
• Vector infrastructure: Strong understanding of semantic search, indexing algorithms, vector databases, and reranking pipelines.
• Vector databases: Experience with Pinecone, OpenSearch, pgvector, Chroma, or comparable vector technologies.
• LLMOps: Experience with prompt versioning, log streaming, latency tracking, cost monitoring, evaluation, and operational metrics.
• AI safety: Knowledge of security guardrails, prompt-injection prevention, data alignment, access controls, and safe tool execution.
• Data pipelines: Experience developing pipelines for structured and unstructured enterprise data, embedding generation, semantic indexing, and evaluation datasets.

• Experience with Model Context Protocol (MCP) ecosystems or contributions to open-source agent libraries.
• Experience fine-tuning models using PEFT or LoRA techniques.
• Familiarity with model monitoring, concept drift, data drift, automated scoring, and evaluation operations.
• Experience with infrastructure as code, containerization, CI/CD, and automated deployment.
• Experience designing cost-efficient and resource-efficient AI systems.
• Exposure to additional AI, data engineering, or cloud-native technologies relevant to production GenAI applications.

• Specific software versions are not mandated in the requirements.
• Candidates should have experience with current, production-supported versions of Python, backend frameworks, agent orchestration frameworks, vector technologies, and AWS services.
• Candidates should be able to assess compatibility, manage upgrades, maintain version control, and support production lifecycle management.
• Experience should include production-level LLM and agentic AI workflows rather than only experimentation or prompt-based prototypes.

• Design and orchestrate multi-agent workflows using planners, executors, retrievers, validators, and human-in-the-loop patterns.
• Implement advanced reasoning mechanics, including ReAct, chain-of-thought, tree-of-thought, and self-reflection loops, with appropriate safety and observability controls.
• Develop memory systems using state graphs, short-term session logs, and long-term knowledge bases to maintain state and context across multi-step workflows.
• Build autonomous task-execution engines with fine-grained tool calling, API integrations, fallback strategies, validation, and escalation controls.
• Deploy and scale foundation models through AWS Bedrock.
• Use AWS Bedrock Knowledge Bases, Agents, Guardrails, and custom model evaluations to develop reliable enterprise AI solutions.
• Develop secure serverless workflows using AWS Lambda and AWS Step Functions to trigger and coordinate agent actions.
• Implement LLMOps practices for prompt versioning, log streaming, latency tracking, quality measurement, and cost optimization.
• Architect end-to-end data-ingestion pipelines for embedding generation, chunking strategies, metadata tagging, and semantic indexing.
• Develop and fine-tune semantic search systems using vector databases and reranking pipelines.
• Design evaluation datasets and data pipelines to track concept drift, data drift, response quality, and automated system scores.
• Integrate unstructured enterprise repositories with LLM applications while maintaining semantic indexing accuracy, data protection, and access controls.
• Translate abstract business processes into deterministic, code-driven workflows and measurable technical outcomes.
• Collaborate with product, engineering, data, security, and operational stakeholders to define requirements and delivery plans.
• Review code and architecture, support technical decision-making, and guide team members through implementation and troubleshooting.
• Optimize AI systems for reliability, safety, scalability, latency, maintainability, sustainability, and cost.
• Maintain technical documentation covering architecture, workflows, prompts, data pipelines, evaluation methods, operating procedures, and known limitations.

• Expert-level Python development experience.

• Expert-level Python development experience.
• Experience building asynchronous backend APIs using FastAPI, Flask, or equivalent frameworks.
• Ability to write modular, maintainable, testable, and production-ready code.
• Ability to develop agent tools, API integrations, data pipelines, evaluation utilities, and automation components.
• Understanding of asynchronous execution, error handling, retries, logging, and workflow state management.

• Experience with additional programming languages used in cloud-native, data, or enterprise application development.
• Experience developing reusable libraries and shared components for AI applications.
• Exposure to event-driven and distributed application development.

• Experience managing structured and unstructured enterprise data for AI applications.

• Experience managing structured and unstructured enterprise data for AI applications.
• Strong understanding of embeddings, chunking, metadata tagging, semantic indexing, retrieval, and reranking.
• Experience with vector databases such as Pinecone, OpenSearch, pgvector, Chroma, or equivalent technologies.
• Understanding of semantic search mechanics and indexing algorithms.
• Ability to design data-ingestion pipelines for RAG applications.
• Experience creating evaluation datasets and data pipelines for automated scoring, data drift, and concept drift analysis.
• Understanding of data quality, lineage, access control, retention, and data alignment.

• Experience with NoSQL databases and knowledge-base technologies.
• Familiarity with data lake, document-processing, and enterprise repository integration patterns.
• Experience with data reconciliation, archival, lifecycle management, and metadata governance.
• Exposure to specialized data preparation for model fine-tuning using PEFT or LoRA.

• Hands-on experience with AWS Bedrock.

• Hands-on experience with AWS Bedrock.
• Experience with AWS EC2, Lambda, IAM, VPCs, and Step Functions.
• Understanding of secure, scalable, and highly available cloud architecture.
• Experience designing serverless workflows for agent execution and orchestration.
• Understanding of cloud identity, networking, service integration, monitoring, and operational support.
• Ability to optimize AI workloads for latency, reliability, resource utilization, and cost.

• Experience with cloud-native deployment automation and infrastructure as code.
• Familiarity with containerization and Kubernetes-based deployments.
• Experience with multi-environment or hybrid cloud application delivery.
• Understanding of sustainable cloud operations and efficient resource allocation.

• LangGraph, CrewAI, AutoGen, Semantic Kernel, or comparable agent orchestration frameworks.

• LangGraph, CrewAI, AutoGen, Semantic Kernel, or comparable agent orchestration frameworks.
• AWS Bedrock Agents, Knowledge Bases, Guardrails, and model evaluation capabilities.
• FastAPI, Flask, or equivalent asynchronous backend API frameworks.
• Vector database and semantic search technologies.
• Frameworks and libraries for embedding generation, retrieval, reranking, and evaluation.
• Libraries supporting tool calling, memory management, workflow state, API integration, and agent observability.
• Experience implementing multi-agent patterns such as planning, execution, delegation, validation, reflection, routing, and human approval.

• Model Context Protocol (MCP) libraries and ecosystem integrations.
• Model-serving, LLMOps, evaluation, and monitoring frameworks.
• Fine-tuning libraries supporting PEFT and LoRA techniques.
• Additional RAG, document-processing, and knowledge-management frameworks.

• Experience across the AI application lifecycle, including requirements analysis, architecture, development, testing, deployment, monitoring, and support.

• Experience across the AI application lifecycle, including requirements analysis, architecture, development, testing, deployment, monitoring, and support.
• Experience developing production-level LLM and agentic AI workflows.
• Experience with Git, code reviews, technical documentation, automated testing, and release practices.
• Ability to define technical approaches, architecture, delivery plans, quality standards, and evaluation criteria.
• Experience implementing LLMOps practices, including prompt versioning, log streaming, latency measurement, quality evaluation, and cost tracking.
• Ability to build prototypes and proof-of-concept solutions and transition suitable solutions into production.
• Experience translating abstract business processes into deterministic, code-driven workflow logic.
• Familiarity with Agile delivery, sprint planning, daily stand-ups, reviews, and retrospectives.

• Experience with CI/CD pipelines, infrastructure as code, containers, and automated deployment.
• Familiarity with experiment tracking, model monitoring, automated regression evaluation, and production alerting.
• Experience with observability, distributed tracing, operational dashboards, and incident response.
• Contributions to open-source agent libraries or MCP ecosystems.

• Knowledge of implementing AI security guardrails and safe tool-calling controls.

• Knowledge of implementing AI security guardrails and safe tool-calling controls.
• Understanding of prompt-injection risks, data leakage, unauthorized tool use, unsafe outputs, and excessive agent autonomy.
• Ability to apply data-access controls, identity management, secrets protection, and least-privilege permissions.
• Understanding of secure API communication, input validation, output validation, and controlled execution.
• Experience checking data alignment, retrieval accuracy, source relevance, and response safety.
• Ability to apply responsible AI principles throughout the design, development, evaluation, and deployment lifecycle.

• Experience with threat modeling for AI agents and LLM applications.
• Familiarity with privacy-preserving AI, model security, auditability, and compliance controls.
• Experience implementing human approval, escalation, and review processes for higher-risk actions.
• Knowledge of security validation within LLMOps, CI/CD, and cloud deployment workflows.

• Overall experience: 3–5+ years of experience in core software development, AI engineering, backend engineering, or