AI Engineer (Agentic LLM Systems)

We’re looking for an experienced AI Engineer who is passionate about building intelligent, tool- using agents leveraging cutting- edge LLM orchestration frameworks. You will be a core contributor to the design, development, and deployment of agentic systems that combine reasoning, memory, retrieval, and orchestration to power the next generation of enterprise AI applications.
About the Role
Core Technical Responsibilities
Agentic Design

Proven ability to architect multi- step agents with planning, tool usage, and self- reflection.
Experience with orchestration frameworks like LangGraph, CrewAI, or custom graph/task- based planners.

LangChain Ecosystem

Deep understanding of Chain, Runnable, and LCEL abstractions.
Experience with tracing & evaluation tools such as LangSmith, Langfuse, or equivalents.
Built custom output parsers, retrievers, and memory components.

Agent Memory

Designed long- term memory using vector stores (FAISS, Pinecone, Milvus, OpenSearch) or knowledge graphs.
Implemented short- term memory (e.g., message history, token windowing).
Strong grasp of performance/cost trade- offs across memory backends.

Retrieval- Augmented Generation (RAG)

Familiar with chunking/embedding strategies, adaptive retrieval, and reranking.
Designed hybrid retrieval pipelines (BM25 + dense vector search).

Prompting & Function Design

Experienced in dynamic prompt injection for real- time tool use.
Skilled in structured prompting (JSON/YAML schemas, function- calling).

LLM Evaluation & Safety

Knowledge of automated evaluation techniques (rubric- based, self- critique).
Familiar with red- teaming, toxicity, bias detection, and PII leakage prevention.

Programming & Tooling

Test- driven development with pytest, coverage tools, and LangChain- specific testing patterns.
Proficient in Python 3.11+ (typing, Pydantic, asyncio) and TypeScript.

AWS Infrastructure (Strong Preference)

Designed serverless workflows using Lambda, Step Functions, and EventBridge.
Hands- on with AWS Bedrock, SageMaker JumpStart, and HuggingFace DLCs.
Experience deploying to ECS/Fargate, EKS with GPU acceleration.
Uses CloudFormation/CDK or Terraform with best- practice IAM and cost monitoring.

MLOps & Observability

Monitoring with OpenTelemetry, CloudWatch, Honeycomb, or similar observability stacks.
Experience in vector- store migrations, prompt/model versioning, and model registries.

Nice to Have

Domain knowledge in financial services or banking is a strong plus.
Knowledge of enterprise AI deployment patterns (multi- tenant RAG, ACL for memory/knowledge base).
Experience with other programming languages such as Java and Go (Golang).
AWS DevOps experience, including CI/CD pipelines, monitoring, and infrastructure automation.
Exposure to OpenAI Assistants API, Semantic Kernel, or open- source agent stacks (AutoGen, SuperAGI, etc.)
Familiarity with GPU cost optimization and caching strategies.

What We Offer

Collaborative environment with strong engineering autonomy.
Competitive compensation and flexible working model.
Opportunity to work at the frontier of AI agent systems with a high- impact product.
Access to cutting- edge AI infrastructure and tooling.

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Cập nhật gần nhất lúc: 2026-03-18 18:30:03