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 Mandatory Skills Description: - 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 Skills Description: - Tesseract OCR
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