Ai engineer

We are building the next generation of AI-powered applications that deliver actionable financial and marketing insights to our clients. As an AI Engineer focused on backend development, you'll be instrumental in designing and developing multiple agentic AI applications that integrate with data lakehouses and business intelligence platforms. This is not a single-project role. You'll work on a pipeline of AI-driven solutions across various use cases, all built on a common architecture leveraging cutting-edge LLM technologies, agentic frameworks, and cloud infrastructure.  What You'll Build Agentic chatbot applications Agentic chatbot applications with conversational UI for financial and marketing insights RAG-based systems RAG-based systems that intelligently retrieve and synthesize information from enterprise data sources MCP (Model Context Protocol) integrations MCP (Model Context Protocol) integrations for tool calling and external system interactions Real-time visualization generation Real-time visualization generation including graphs, charts, and dynamic dashboards Sandboxed code execution environments Sandboxed code execution environments for safe AI-generated code runs LLM benchmarking systems LLM benchmarking systems to evaluate and switch between models (AWS Bedrock, Azure AI Foundry) as new versions emerge Data integration pipelines Data integration pipelines connecting to SQL-based data lakehouses and Tableau dashboards Key Responsibilities Development Design and develop Python backend services using Fast API Fast API for AI-powered applications Implement agentic workflows using Lang Chain Lang Chain and/or Azure AI agentic frameworks like Auto Gen Azure AI agentic frameworks like Auto Gen Build and maintain RAG systems for context-aware AI responses Develop MCP servers and integrations for tool calling and external API interactions Create sandboxed execution environments for AI-generated code and visualizations Data & SQL   Write efficient SQL queries against data lakehouses to retrieve business insights Integrate with Tableau dashboards and other BI platforms Optimize data retrieval patterns for LLM context windows AI/ML Operations   Build benchmarking systems to evaluate LLM performance across different models and providers Implement model switching logic to automatically use the most efficient/effective LLM Monitor and optimize token usage, costs, and response latency Work with AWS Bedrock and Azure AI Foundry for model deployment   Dev Ops & Deployment   Containerize applications using Docker Deploy and manage services in Kubernetes (K8s) clusters Build and maintain Git Lab CI/CD pipelines for automated testing and deployment Ensure scalability, reliability, and security of production systems   Collaboration   Work directly with US-based clients to understand requirements and deliver solutions Collaborate with frontend developers (React), data engineers, and architects Participate in technical design discussions and architectural decisions Document code, APIs, and system architecture clearly Required Qualifications Technical Skills   3+ years of professional Python development experience Strong SQL skills with experience querying databases or data lakehouses Experience with Fast API or similar Python web frameworks (Flask, Django) Hands-on experience with AWS Bedrock or Azure AI services Familiarity with Docker containerization Experience with Kubernetes (K8s) deployments Proficiency with Git and Git Lab CI/CD or similar CI/CD tools Understanding of RESTful API design and development   AI/ML Experience   Working knowledge of LLM technologies (GPT, Claude, Llama, etc.) Experience with prompt engineering and LLM application development Familiarity with Lang Chain, Auto Gen, Azure AI frameworks, or similar agentic/orchestration frameworks Understanding of RAG (Retrieval-Augmented Generation) patterns   Soft Skills   Excellent English communication skills (written and verbal) for US client interaction Ability to work autonomously and complete tasks with minimal supervision Strong problem-solving skills and attention to detail Self-motivated with ability to manage priorities and deadlines   Preferred Qualifications   Experience with React or frontend development (bonus, not required) Familiarity with Tableau or other BI platforms Experience with data lakehouses (Databricks, Snowflake, etc.) Knowledge of Model Context Protocol (MCP) or similar tool-calling frameworks Experience building benchmarking or evaluation systems for ML models Background in financial services or marketing analytics Contributions to open-source AI/ML projects