Senior AI Engineer

Luxoft; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

Luxoft in Singapore seeks experienced AI Engineers to lead a strategic initiative building Agentic AI use cases around Trading Surveillance. You will design, develop, and maintain full-stack Python applications with modern frontend frameworks and build RAG systems for AI applications.

The role requires 6–9 years of AI/ML solution experience and 3+ years data engineering, with strong skills in data pipelines, ETL/ELT, and vector databases.

Qualifications

• 6–9 years of AI/ML solution implementation experience.
• 3+ years of data engineering (ETL/ELT, data warehousing, batch/streaming).
• Experience with Agentic AI concepts and RAG implementations.
• Strong Python and API development skills (FastAPI).
• Experience with vector databases (Pinecone, Milvus, Weaviate).

Responsibilities

• Design, develop, and maintain full-stack Python applications with modern frontend frameworks.
• Build and optimize RAG systems for AI applications.
• Create and implement vector databases and knowledge stores.
• Develop APIs connecting frontend interfaces with AI services.
• Implement and maintain CI/CD pipelines for AI apps.
• Monitor production systems and troubleshoot performance issues.
• Design and maintain data pipelines, ETL/ELT workflows, and data infrastructure.

Skills

AI/ML solution design

Full-stack AI integration

Multi-Cloud AI infrastructure

Prompt engineering & LLM fine-tuning

RAG systems

Knowledge graphs

LLMOps monitoring

Data engineering

ETL/ELT pipelines

Data warehousing

Python (FastAPI)

LangChain

OpenAI models & LLMs

Vector databases

Tools

Airflow

Prefect

Dagster

Snowflake

BigQuery

Redshift

Databricks

Kafka

Spark

Job description

Project description

We are looking for AI Engineers for a strategic initiative to build Agentic AI use cases around Trading Surveillance.

Responsibilities

• Design, develop, and maintain full-stack Python applications with modern frontend frameworks
• Build and optimize RAG (Retrieval-Augmented Generation) systems for AI applications
• Create and implement efficient vector databases and knowledge stores
• Develop APIs that connect frontend interfaces with backend AI services
• Implement and maintain CI/CD pipelines for AI applications
• Monitor application performance and troubleshoot issues in production
• Design and maintain data pipelines, ETL/ELT workflows, and data infrastructure to support AI and analytics workloads

SKILLS

Must have

• Minimum of 6 to 9 years of experience in AI/ML solution implementation
• Minimum of 3 years of experience in Data Engineering (data pipelines, ETL/ELT, data warehousing, batch/streaming processing)
• AI & ML Skills:Multi-Agent System Design & OrchestrationFull-stack AI Integration (Frontend to Backend)Multi-Cloud AI Infrastructure (AWS Bedrock, Vertex AI, Azure AI, Snowflake Cortex)Advanced Prompt Engineering & LLM Fine-tuningAgentic Retrieval Augmented Generation (RAG)AI Safety, Guardrails & GovernanceEnterprise Application Integration & AI OrchestrationSemantic Modeling & Knowledge Graph ConstructionHigh-Scale Vector Database ManagementLLM Observability, Evaluation & Monitoring (LLMOps)
• Data Engineering Skills:Data pipeline design & orchestration (Apache Airflow, Prefect, Dagster)ETL/ELT development and optimizationData warehousing & lake architecture (Snowflake, BigQuery, Redshift, Databricks)Batch and streaming data processing (Spark, Kafka, Flink)Data modeling and schema designSQL proficiency and query optimization
• Main Technologies:Python (FastAPI / Pydantic)LangChain & LangGraphEdio & Render (Deployment & Hosting)Streamlit & Gradio (UI/UX for AI)OpenAI & Open-Source Models (Llama 3, Mistral)Vector Databases (Pinecone, Milvus, Weaviate)Cloud Platforms: AWS Bedrock, Google Vertex AI, Azure AI StudioLLM Monitoring Tools (LangSmith / Arize Phoenix)Data Tools: Apache Spark, Airflow, Kafka, dbt, Snowflake

Nice to have

Tesseract OCR