Required Skills
Deep knowledge of multi-agent architectures including planners, executors, and tool routing.
Strong understanding of RAG systems: chunking, embeddings, vector/hybrid search, and retrieval policies.
Experience evaluating LLMs and agent workflows incorporating statistical reasoning and validation.
Proficiency with AWS (Lambda, ECS/EKS, S3, API Gateway, EC2, IAM) and Infrastructure-as-Code for cloud resource automation and deployment.
Experience with observability tools (Datadog, logging, tracing, metrics).
Familiarity with PostgreSQL, DBT, data modeling, schema evolution, and performance tuning.
Hands on experience on AI
AI Engineer who has extensive knowledge on LLM creation, AI tool adoption and also defining the frameworks
should be strongly vocal enough to communicate with the customers
Design, develop, and maintain LLM-powered multi-agent workflows for code analysis, remediation proposals, and safe patch generation.
Implement agentic patterns including planning/execution loops, dynamic tool orchestration, sandboxing, guardrails, and failure recovery.
Build scalable automation systems for technical debt remediation: language/runtime upgrades, vulnerability patching, dependency modernization, and config drift correction.
Partner with Dev Experience and Platform teams to define engineering guidelines and reusable standards across the organization.
Architect and optimize Retrieval-Augmented Generation (RAG) pipelines, managing chunking, embeddings, hybrid search, reranking, and retrieval policies.
Develop robust evaluation frameworks for LLMs, RAG, and agent workflows, including offline datasets, validation metrics, statistical testing, and A/B tests.
Contribute to backend systems using Python, distributed systems, microservices, PostgreSQL, DBT, vector databases, caching, streaming, and queueing.
Build CI/CD pipelines, observability dashboards, and perform performance analysis on model, retrieval, and network layers.
Collaborate cross-functionally with product, platform, and security to move prototypes to production-grade services.
Communicate clearly with stakeholders, write technical documentation, and mentor junior engineers.
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Deep knowledge of multi-agent architectures including planners, executors, and tool routing.
Strong understanding of RAG systems: chunking, embeddings, vector/hybrid search, and retrieval policies.
Experience evaluating LLMs and agent workflows incorporating statistical reasoning and validation.
Proficiency with AWS (Lambda, ECS/EKS, S3, API Gateway, EC2, IAM) and Infrastructure-as-Code for cloud resource automation and deployment.
Experience with observability tools (Datadog, logging, tracing, metrics).
Familiarity with PostgreSQL, DBT, data modeling, schema evolution, and performance tuning.
Hands on experience on AI
AI Engineer who has extensive knowledge on LLM creation, AI tool adoption and also defining the frameworks
should be strongly vocal enough to communicate with the customers
Design, develop, and maintain LLM-powered multi-agent workflows for code analysis, remediation proposals, and safe patch generation.
Implement agentic patterns including planning/execution loops, dynamic tool orchestration, sandboxing, guardrails, and failure recovery.
Build scalable automation systems for technical debt remediation: language/runtime upgrades, vulnerability patching, dependency modernization, and config drift correction.
Partner with Dev Experience and Platform teams to define engineering guidelines and reusable standards across the organization.
Architect and optimize Retrieval-Augmented Generation (RAG) pipelines, managing chunking, embeddings, hybrid search, reranking, and retrieval policies.
Develop robust evaluation frameworks for LLMs, RAG, and agent workflows, including offline datasets, validation metrics, statistical testing, and A/B tests.
Contribute to backend systems using Python, distributed systems, microservices, PostgreSQL, DBT, vector databases, caching, streaming, and queueing.
Build CI/CD pipelines, observability dashboards, and perform performance analysis on model, retrieval, and network layers.
Collaborate cross-functionally with product, platform, and security to move prototypes to production-grade services.
Communicate clearly with stakeholders, write technical documentation, and mentor junior engineers.
#J-18808-Ljbffr