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 BuildAgentic 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 dashboardsKey ResponsibilitiesDevelopment Design and develop Python backend services using FastAPI FastAPI for AI-powered applications Implement agentic workflows using LangChain LangChain and/or Azure AI agentic frameworks like AutoGen Azure AI agentic frameworks like AutoGen 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 visualizationsData & SQLWrite 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 windowsAI/ML OperationsBuild 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 deploymentDevOps & DeploymentContainerize applications using Docker Deploy and manage services in Kubernetes (K8s) clusters Build and maintain GitLab CI/CD pipelines for automated testing and deployment Ensure scalability, reliability, and security of production systemsCollaborationWork 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 clearlyRequired QualificationsTechnical Skills3+ years of professional Python development experience Strong SQL skills with experience querying databases or data lakehouses Experience with FastAPI 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 GitLab CI/CD or similar CI/CD tools Understanding of RESTful API design and developmentAI/ML ExperienceWorking knowledge of LLM technologies (GPT, Claude, Llama, etc.) Experience with prompt engineering and LLM application development Familiarity with LangChain, AutoGen, Azure AI frameworks, or similar agentic/orchestration frameworks Understanding of RAG (Retrieval-Augmented Generation) patternsSoft SkillsExcellent 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 deadlinesPreferred QualificationsExperience 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