Senior AI Engineer, Agentic Systems
New
Fully remote and flexible work environment within the United States.Full-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5–8+ years
- Required Skills
- PythonTypeScriptCI/CDLLMLangChain
Requirements
- 5–8+ years of experience in software engineering, platform engineering, or AI systems development with recent hands-on experience building production LLM applications.
- Strong expertise with agentic AI frameworks such as LangGraph, LangChain Agents, AutoGen, CrewAI, LlamaIndex Agents, Semantic Kernel, or Haystack Agents.
- Deep experience designing and optimizing RAG pipelines, vector database architectures, chunking strategies, embeddings, and retrieval systems.
- Proven track record building secure, observable, scalable, and cost-efficient AI systems in production environments.
- Strong programming skills in Python and/or TypeScript with experience in APIs, asynchronous programming, testing, CI/CD, and containerized environments.
- Hands-on experience with tracing, evaluation frameworks, guardrails, identity and access management, secrets handling, and PII protection.
- Strong understanding of AI safety, governance, compliance, and enterprise security standards such as ISO 27001, SOC 2, HIPAA, or GDPR.
- Excellent communication skills with the ability to collaborate with technical teams, business stakeholders, and enterprise clients.
- Experience with Azure AI services, Azure OpenAI, Azure Functions, AKS, or cloud-native AI infrastructure is highly valued.
Responsibilities
- Design and implement scalable multi-agent architectures with advanced orchestration, memory management, routing, and policy enforcement capabilities.
- Evaluate and deploy leading agentic frameworks such as LangGraph, LangChain Agents, AutoGen, CrewAI, LlamaIndex Agents, Semantic Kernel, or Haystack Agents.
- Build reusable AI system components including planners, evaluators, tool registries, policy guards, and workflow orchestration modules.
- Integrate enterprise tools, APIs, structured data sources, and event-driven systems through function calling, webhooks, and automation pipelines.
- Develop and optimize retrieval-augmented generation (RAG) architectures using vector databases, structured knowledge systems, and advanced grounding strategies.
- Implement reliability mechanisms such as tracing, observability, monitoring dashboards, caching, circuit breakers, and cost optimization frameworks.
- Build evaluation pipelines and testing frameworks to assess task success, groundedness, safety, latency, and operational performance.
- Establish CI/CD pipelines, deployment workflows, feature flag systems, and production release strategies for AI applications.
- Enforce AI safety, governance, privacy, and compliance standards including role-based access control, data minimization, and policy enforcement.
- Collaborate directly with enterprise clients and internal teams to translate business workflows into scalable AI-driven solutions and production deployments.
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