Senior AI Engineer - Agentic Systems & Data Pipelines at Collaboration.ai | chakrie

Requirements

7+ years of professional software engineering experience, with 3+ years focused on AI/ML or data engineering
Production agentic/LLM application experience — built and operated systems around LLM APIs (Anthropic, OpenAI) serving real users: agents, tool-use, or orchestrated LLM workflows
Data engineering background — robust, scalable pipelines for AI/ML workloads
LLM operations experience — evals and observability for production LLM systems (quality, cost, latency)
Production retrieval experience — vector databases and/or search engines (OpenSearch, Elasticsearch)
Modern Python stack proficiency — FastAPI, Pydantic, async/await, modern dependency management
AI-native workflows — demonstrated ability to leverage Claude Code/Codex or similar agentic coding tools to accelerate development
Experience with Docker, Kubernetes, and AWS
US citizenship required (DoD contracting — IL4/IL5 environments — and FedRAMP compliance)

Nice-to-Haves

Deep agentic ecosystem experience — Agent Skills standards, custom MCP servers, agent SDKs across major vendors
Advanced RAG expertise — GraphRAG, agentic RAG, contextual retrieval, reranking strategies
Graph data experience — knowledge graphs, graph databases, or graph-based retrieval
Model selection & rightsizing — matching models to domain-specific use cases across quality, cost, and latency tradeoffs
Streaming data experience (Kafka, Kinesis) for real-time knowledge base updates
Research background, open-source contributions, or an advanced degree in ML/IR/NLP
Languages: Python (primary); Kotlin (core platform language at CAI); TypeScript/Node.js and other modern languages (secondary)
AI/ML: FastAPI, Pydantic; multi-provider LLM SDKs (Anthropic, OpenAI, and others)
Agentic Tooling: Claude Code/Codex/etc.; industry-leading agent SDKs and harnesses; MCP servers; Agent Skills standards
LLM Operations: Langfuse + evals (golden datasets, LLM-as-judge); in-house FinOps tracking (token usage, latency, cost); multi-provider orchestration including AWS Bedrock
Search & Retrieval: Vector databases, OpenSearch, embedding models
Data: PostgreSQL, Amazon S3; streaming pipelines (Kafka/Kinesis) where needed
Infrastructure: Docker, Kubernetes (AWS EKS); DataDog + OpenTelemetry observability

Responsibilities

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Skills & expertise

Qualifications, experience and the skills that matter most, along with anything that would make you stand out.

Practical next steps, the documents to prepare and the dates to keep in mind before you apply or get in touch.

Contact details, links to the official source and the notes our team adds after checking each listing by hand.

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Salary & benefits

A clear overview of what is expected, written by the people who posted it, with every detail you need to decide.

Qualifications, experience and the skills that matter most, along with anything that would make you stand out.

Practical next steps, the documents to prepare and the dates to keep in mind before you apply or get in touch.

Contact details, links to the official source and the notes our team adds after checking each listing by hand.

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