MidFull TimeEN
We are seeking an experienced Data & AI Engineer to join our dynamic Finance AI team. The successful candidate will play a central role in designing, building, and maintaining robust, efficient cloud orchestration systems within our data lake architecture. While the primary focus is on data engineering but also architect the "intelligence layer" - building RAG (Retrieval-Augmented Generation) systems and agentic workflows that automate complex financial analysis.
The ideal candidate will possess excellent collaborative skills and be able to work seamlessly with data scientists, technical professionals, and end users across finance functions. We value candidates who have a knack for empowering business partners and guiding finance teams towards a self-service, do-it-yourself approach.
Design, implement, and maintain scalable cloud-based data lake and pipelines, leveraging Databricks for robust, high-performance solutions.
Develop efficient data pipelines for ingesting, processing, and storing large datasets, incorporating best practices for data quality, lineage tracking, and metadata management - specifically optimized for LLM consumption.
Enforce data quality checks, monitor pipeline health, and continuously optimize reliability, scalability, and performance.
RAG & Agentic Systems: Move beyond "naive RAG" to implement hybrid search, re-ranking, and contextual compression to handle dense financial documents and tables.
Agentic Workflows: Design and deploy multi-agent systems using LangGraph, CrewAI, or Agent Bricks.
Evaluation: Implement rigorous "LLM-as-a-Judge" frameworks to quantify accuracy, faithfulness, and latency.
Deploy security layers to ensure AI outputs comply with financial regulations and privacy standards.
Create reusable "Agent templates" that empower Finance business partners to build their own safe AI-driven workflows.
Work closely with AI teams and key stakeholders across finance to understand data requirements for AI systems and ensure their smooth deployment
Provide support and guidance on data engineering best practices and tools to other team members.
Guide business functions and personnel in developing do-it-yourself capabilities for data tasks.
Establish efficient workflows with business functions to ensure prompt and high-quality data movement from source systems to production.
3-5 years of experience in data engineering, with a focus on data lake and cloud infrastructure and a proven shift into GenAI implementation (RAG, Agents, or LLM-integrated apps).
Experience with Databricks and building data pipelines in cloud environments (e.g. AWS, Azure, or GCP).
Experience with data orchestration tools such as Apache Airflow, Prefect, Luigi or Dagster.
Proficiency in SQL and Python (Async, FastAPI) or other programming languages to develop custom automation scripts, tables, and solutions. You should be a "power user" of AI-native IDEs like Cursor/claude code to accelerate development and refactoring.
Experience implementing MCP Servers to expose databases and local tools to LLMs. Traditional APIs: Strong experience with REST/GraphQL/FastAPI for building the deterministic "hooks" that AI agents call.
Experience with CI/CD and DevOps practices, version control (e.g., Git), and infrastructure as code (e.g., Terraform, Ansible).
Strong understanding of data engineering best practices, including ETL processes, data quality management, and pipeline monitoring.
Strong problem-solving skills and the ability to troubleshoot complex data pipeline issues.
Familiarity with data governance principles and data privacy regulations like GDPR and CCPA.
Ability to ensure orchestration workflows are built end-to-end for seamless integration with state-of-the-art ML and AI products. Knowledge of ML frameworks and tools (e.g., scikit-learn, MLflow, TensorFlow, or similar) is a plus.
Excellent communication and collaboration skills to work effectively with data scientists, solutions architects, and business professionals.
Knowledge of metadata systems and data lineage tracking tools.
Affinity for guiding business functions in adopting do-it-yourself methodologies.
An affinity for machine learning engineering or an intention to further develop skills in this area is highly valued.
Experience with data warehousing concepts and platforms like Snowflake, Google BigQuery.
A basic understanding of financial concepts and terminology.
