Job Description

This role offers a unique opportunity to contribute to the development of cutting-edge AI workflow orchestration and automation platforms, focusing on RAG (Retrieval-Augmented Generation), tool invocation, Agent collaboration, and asynchronous orchestration. You will be instrumental in implementing innovative solutions like Diffy or similar "shadow traffic replay + response differential comparison" methodologies for model/version regression testing and gradual deployment.

Key Responsibilities

AI/LLM Workflow Orchestration: Design and implement multi-step reasoning, Agent collaboration, tool invocation (Tool-Calling/Function-Calling), asynchronous task queues, and compensation mechanisms. Optimize RAG pipelines including data ingestion, chunking, vectorization, retrieval/reranking, context compression, caching, and cost reduction.
Evaluation & Quality Assurance: Establish automated evaluation and alignment systems (benchmark sets, Ragas/G-Eval/custom metrics), integrate A/B testing, and implement real-time monitoring. Utilize Diffy (or equivalent) for shadow traffic replay and response differential analysis to identify regression risks in models/prompts/service upgrades, supporting canary releases and rapid rollbacks.
Engineering & Observability: Build version control for models/prompts, feature/data versioning, experiment tracking (MLflow/W&B), and audit logs. Implement end-to-end observability covering latency, error rates, prompt/context length, hit rates, and cost monitoring (tokens/$).
Platform Integration: Expose workflows via API/SDK/microservices; integrate with business backends (Go/PHP/Node), queues (Kafka/RabbitMQ), storage (Postgres/Redis/object storage), and vector databases (Milvus/Qdrant/pgvector). Ensure security and compliance (data anonymization, PII protection, auditing, rate limits, quotas, and model governance).

Job Requirements

Mandatory: 3+ years in backend/data/platform engineering with 1–2 years of hands-on LLM/generative AI project experience.
Proficiency in LLM application engineering: prompt engineering, function/tool calling, dialogue state management, memory, structured output, alignment, and evaluation.
Familiarity with at least one orchestration framework: LangChain/LangGraph, LlamaIndex, Temporal/Prefect/Airflow, or custom DAG/state machine/compensation solutions.
End-to-end RAG experience (data cleaning → vectorization → retrieval → reranking → evaluation); knowledge of Milvus/Qdrant/pgvector.
Experience with Diffy or equivalent traffic replay/differential analysis (shadow traffic, recording/replay, regression output comparison, canary releases).
Strong engineering fundamentals: Docker, CI/CD, Git, logging/metrics (OpenTelemetry/Prometheus/Grafana).
Proficiency in Go/Python/TypeScript; ability to develop reliable services and tests.
Excellent remote collaboration and documentation skills; metrics-driven delivery.

Preferred Qualifications

Deep Diffy expertise (or integrating with API gateways for shadow traffic/routing/comparison).
LLMOps/evaluation platform experience (Arize Phoenix, Evidently, PromptLayer, OpenAI Evals, Ragas).
Practical Agent framework implementations (LangGraph, autogen/crewAI, GraphRAG, tool ecosystems).
Security/compliance knowledge (anonymization, PDPA/GDPR, moderation tools like Llama Guard).
Domain experience in IM/customer service/marketing automation or multilingual scenarios (Chinese/English/Vietnamese).
Cost optimization techniques: caching, retrieval compression, model routing/multi-provider switching (OpenAI/Anthropic/Google/local models).

Tech Stack (Partial)

Orchestration/Agent: LangChain/LangGraph, LlamaIndex, Temporal/Prefect/Airflow
Models & Evaluation: OpenAI/Anthropic/Google, VLLM/Ollama, Ragas, G-Eval, MLflow/W&B
Retrieval: Milvus, Qdrant, pgvector, Elasticsearch, rerankers (bge/multilingual/E5)
Services: Go/Python/TypeScript, gRPC/REST, Redis, Postgres, Kafka/RabbitMQ, Docker/K8s
Observability: OpenTelemetry, Prometheus, Grafana, ELK/ClickHouse
Diff/Replay: Twitter Diffy or equivalent shadow traffic/replay systems

Benefits

Fully remote work environment with a collaborative team culture and competitive compensation package.

🤖

AI Job Analysis

Intelligent Job Recommendations Based on Deep Learning

Ready for Analysis

Analyze Job Core Requirements

This Algorithm Engineer role at Deepcoin demands expertise in AI workflow orchestration, particularly focusing on RAG, Agent collaboration, and asynchronous task management. The position requires hands-on experience with LLM projects and strong backend engineering skills.

3+ years in backend/data/platform engineering with 1–2 years of LLM/generative AI experience
Proficiency in LLM application engineering (prompt engineering, tool calling, dialogue state management)
Familiarity with orchestration frameworks like LangChain/LangGraph or LlamaIndex
End-to-end RAG pipeline experience (data cleaning to evaluation)
Strong engineering fundamentals (Docker, CI/CD, Git, logging/metrics)

Understand Employer Recruitment Intent

Deepcoin, likely operating in the cryptocurrency or blockchain space, is seeking an Algorithm Engineer to enhance their AI-driven workflow automation platforms. The emphasis on RAG, Agent collaboration, and asynchronous orchestration suggests they're building sophisticated AI systems for financial data processing or trading automation. The requirement for Diffy-like regression testing indicates a need for robust, reliable systems where model accuracy is critical - potentially for real-time decision making in volatile markets. Their tech stack preferences and focus on observability/security point to enterprise-grade AI infrastructure development.

MyJob Interviewer Says

As an interviewer for this role, I would prioritize candidates who demonstrate practical experience implementing RAG pipelines and Agent frameworks in production environments. Strong examples of handling version control for models/prompts and implementing evaluation systems would be particularly valuable. Given Deepcoin's likely financial applications, I'd pay special attention to candidates with experience balancing model performance against operational costs and latency requirements.

AI Matching Suggestions

To optimize your application for this Deepcoin position, focus on demonstrating your AI orchestration expertise and financial domain awareness.

Highlight specific RAG pipeline projects in your resume, quantifying performance improvements
Showcase experience with traffic replay systems (Diffy or equivalent) for model testing
Prepare concrete examples of cost optimization strategies you've implemented in LLM projects
Demonstrate knowledge of security/compliance considerations in financial AI applications
Be ready to discuss your approach to balancing model accuracy with system latency requirements

Algorithm Engineer at Deepcoin

Employment Information

Job Description

Key Responsibilities

Job Requirements

Preferred Qualifications

Tech Stack (Partial)

Benefits

AI Job Analysis

Analyze Job Core Requirements

Understand Employer Recruitment Intent

MyJob Interviewer Says

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Algorithm Engineer at Deepcoin

Employment Information

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Key Responsibilities

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Preferred Qualifications

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Benefits

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New Things Will Always
Update Regularly