• Leena Vu
  • Software Engineer - AI/LLM Applied

Software Engineer - AI/LLM Applied

Engineering Backend API Python AI/ML SQL

Icon salary 年収
交渉可能
Icon Location Location
Ho Chi Minh

Benefits

13ヶ月目の給与 13ヶ月目の給与
仕事用のノートパソコン/デスクトップ 仕事用のノートパソコン/デスクトップ
その他の福利厚生 その他の福利厚生
在宅勤務 在宅勤務

Job Overview And Responsibility

The Opportunity We are building data analytics agents that leverage LLMs to help users extract insights from our data platform (Databricks). These agents must go beyond natural-language interfaces—they are expected to reason over data, generate correct queries, interpret results, and deliver actionable insights that business users can trust. This role is responsible for designing systems where LLMs interact safely and reliably with production data, under real-world constraints such as data scale, schema complexity, governance, and performance. Key Responsibilities - Architect and Scale Core Systems: Lead the design, implementation, and optimization of highly scalable, fault-tolerant backend services and non-blocking APIs using Python (FastAPI) . Ensure services handle high throughput with minimal latency. - End-to-End Product Delivery: Drive the full lifecycle of product features—from requirements gathering and prototyping through testing, deployment, and iteration—working closely with product managers, UX designers, and DevOps. - Champion Asynchronous Patterns: Build and maintain event-driven, asynchronous processing pipelines (e.g., message queues, pub/sub) to support real-time data ingestion, processing, and integrations. - Performance & Reliability: Establish and enforce best practices for monitoring, observability, and automated testing. Identify bottlenecks and implement caching, connection pooling, backpressure, and rate-limiting strategies to sustain 99.9%+ uptime. - Comprehensive Service Monitoring: Design, deploy, and maintain end-to-end observability solutions using Prometheus for metrics collection and Grafana for visualization and alerting. - AI & RAG Applications: Architect Retrieval-Augmented Generation (RAG) solutions and intelligent agents that solve real-world problems—integrating vector stores, LLM APIs (e.g., OpenAI), and custom prompting frameworks. - Mentorship & Collaboration: Provide technical leadership and mentorship to mid-level engineers, foster a culture of code quality, and collaborate across cross-functional teams to drive continuous improvement.

Required Skills and Experience

- 5+ years of software engineering experience, with a proven track record of building and operating production-grade, scalable applications. - Expertise in Python & FastAPI: Deep familiarity with asynchronous programming (async/await in Python or non-blocking event loops in Java), concurrency models, and profiling/tuning of services. - API Design Mastery: Hands-on experience building RESTful, GraphQL, or gRPC APIs that support high request volumes; strong understanding of HTTP/2, WebSockets, and protocol optimizations. - Cloud & Infrastructure: Proficient with AWS, GCP, or Azure; containerization with Docker; orchestration using Kubernetes/Helm; Infrastructure-as-Code (Terraform, CloudFormation). - Data Storage & Caching: Solid background in relational (PostgreSQL, MySQL) and NoSQL (MongoDB, Redis, DynamoDB) databases; designing data schemas for performance and scalability. - Asynchronous Ecosystems: Experience with message brokers (Kafka, RabbitMQ, AWS SQS), task queues (Celery, AWS Lambda) or Java equivalents, and stream-processing frameworks.

Why Candidate should apply this position

- Competitive package - Hybrid working model - Macbook Pro provided for work - Bonus: 13th month salary - Weekly learning sessions and offsites - Close-knit small team culture

Preferred skills and experiences

- RAG & Agent Development: Practical experience implementing Retrieval-Augmented Generation pipelines, vector search (e.g., Pinecone, Weaviate), and building autonomous agents with LangChain or similar frameworks. - AI/ML Collaboration: Familiarity with embedding-based search, prompt engineering, chaining, and orchestration of multi-LLM workflows. - Observability & Security: Strong grasp of logging, tracing (OpenTelemetry), metrics (Prometheus/Grafana), and security best practices (OAuth2, JWT, encryption). - Leadership & Communication: Excellent verbal and written English skills; demonstrated ability to influence technical direction and collaborate effectively across teams.

Report to

Chief Digital Officer

Interview process

Overview interview -> Technical interview (90mins) -> Fit interview (in English)

Leena Vu

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