Automation Engineer Submission Data and Content Generation

Job Summary

We are hiring a GenAI Agent Developer to join the AIDCG initiative, a transformative program focused on automating complex, multi-step document generation workflows through state-of-the-art AI agents.

This role blends advanced GenAI engineering, open-source innovation, and AWS cloud expertise to deliver production-grade AI agents that are accurate, scalable, and highly secure. You will design orchestration frameworks that transform raw, unstructured data into highly structured, compliant outputs.

Job Responsibilities

• Agentic Architecture: Design, deploy, and scale multi-agent orchestration systems and autonomous workflows using cutting-edge frameworks.
• Advanced RAG Pipelines: Build and optimize advanced retrieval-augmented generation (RAG) pipelines over massive, heterogeneous datasets (structured and unstructured).
• State Memory Management: Implement robust state management, short/long-term memory systems, and self-correction/reflection loops within agent networks.
• Evaluation Guardrails: Create and implement robust evaluation metrics, observability pipelines, and guardrails for content quality, hallucination reduction, bias mitigation, and safety standards.
• Performance Optimization: Monitor and optimize AI inference cost, latency, throughput, token usage, and overall system reliability.
• Security Access Control: Implement robust access controls, data encryption, user authentication, and prompt injection mitigation across all LLM workflows.
• Collaboration Best Practices: Document and share reusable agent patterns, prompt libraries, and engineering components across cross-functional technical teams.

Education / Experience

• Demonstrated experience taking ownership of ambiguous tasks, successfully driving small to medium initiatives, and acting as a technical mentor.
• Proven track record of engaging in knowledge-sharing initiatives, speaking at internal technical events, and navigating group dynamics in diversified settings.

Technical Skills

• GenAI Development: Advanced prompt engineering, fine-tuning, RAG/GraphRAG, schema-constrained outputs, function/tool-calling, and semantic caching.
• Agentic Frameworks: LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, DSPy.
• Vector Databases: FAISS, Milvus, Qdrant, Pinecone, Weaviate, Pgvector.
• LLM Serving Infra: vLLM, Hugging Face TGI, Ollama (for local development).
• Guardrails Validation: NeMo Guardrails, Guardrails AI, Pydantic, Instructor.
• LLM Ops Observability: Ragas, DeepEval, Langfuse, LangSmith, Phoenix.
• Data Parsing: Unstructured, Apache Tika, LlamaParse, PDFPlumber.
• Programming DevOps: Python (FastAPI, asyncio), REST/GraphQL APIs, Git, CI/CD pipelines, Docker, Kubernetes, OpenTelemetry.
• Emerging Protocols: Model Context Protocol (MCP) and Agent2Agent communication standards.

AWS Ecosystem (Baseline Experience)

• Amazon Bedrock: Foundation model access, custom configurations, and managed agent workflows.
• Amazon SageMaker: Fine-tuning, hosting, evaluating, and deploying open-source LLMs.
• Amazon OpenSearch Service: Vector search, hybrid search, and enterprise retrieval infrastructure.
• AWS Step Functions: Multi-step orchestration and state machine management for hybrid AI/traditional pipelines.

Additional Qualifications

• Problem-Solving: An analytical mindset capable of breaking down highly abstract, ambiguous logic loops into predictable agent behaviors.
• Collaboration: Ability to thrive in a fast-paced, product-focused agile engineering environment alongside data scientists and product owners.
• Communication: Strong technical writing and communication skills for documenting complex system architectures and cross-functional collaboration.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.