Senior AI Engineer

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 & Orchestration
• Full-stack AI Integration (Frontend to Backend)
• Multi-Cloud AI Infrastructure (AWS Bedrock, Vertex AI, Azure AI, Snowflake Cortex)
• Advanced Prompt Engineering & LLM Fine-tuning
• Agentic Retrieval Augmented Generation (RAG)
• AI Safety, Guardrails & Governance
• Enterprise Application Integration & AI Orchestration
• Semantic Modeling & Knowledge Graph Construction
• High-Scale Vector Database Management
• LLM Observability, Evaluation & Monitoring (LLMOps)
• Data Engineering Skills:
• Data pipeline design & orchestration (Apache Airflow, Prefect, Dagster)
• ETL/ELT development and optimization
• Data warehousing & lake architecture (Snowflake, BigQuery, Redshift, Databricks)
• Batch and streaming data processing (Spark, Kafka, Flink)
• Data modeling and schema design
• SQL proficiency and query optimization
• Main Technologies:
• Python (FastAPI / Pydantic)
• LangChain & LangGraph
• Edio & 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 Studio
• LLM Monitoring Tools (LangSmith / Arize Phoenix)
• Data Tools: Apache Spark, Airflow, Kafka, dbt, Snowflake

Nice to have

• Tesseract OCR