AI Automation and Data Engineer: Finance/Investment Management

Company Description

Vertus Solutions & Services implements innovative technology solutions for finance and investment management organizations. Blending industry expertise with advanced technology solutions, we enable our clients to stay competitive. Our team values collaboration, innovation, and professional growth, cultivating an environment where employees thrive making meaningful impact.

Role Description

This role involves designing, developing, and optimizing data pipelines, data models, and automating workflows to enhance data quality, reliability and available to enable data-driven decision-making. Workflow automation involves Agentic Automation solutioning with AI-driven autonomous systems (agents) that can plan, reason, and execute multi-step tasks with minimal human intervention. Other responsibilities include include designing and implementing data integration solutions involving ETLs/ DAGs, analyzing data for insights, and collaborating with cross-functional teams to integrate AI and automation technologies into financial systems.

Qualifications

• Proficiency in Data Engineering, design and implementation experience of robust large data solutions
• Strong expertise in ETL, DAG etc. and related tools.
• Knowledge and practical experience with Data Warehousing and Data Analytics.
• AI and machine learning principles, particularly related to finance.
• Problem-solving skills and the ability to work independently or collaboratively on complex projects.
• Bachelor's or advanced degree in Computer Science, Data Science, Finance, or related field.

Agent Design & Development

• Architect and build AI agents using frameworks, design multi-agent pipelines where specialized agents collaborate to complete complex workflows
• Implement tool use, function calling, and MCP (Model Context Protocol) integrations to extend agent capabilities

Orchestration & Workflow Automation

• Build agentic loops with robust planning, memory, and decision-making logic
• Define task decomposition strategies so agents break large goals into executable subtasks
• Integrate agents with APIs, databases, browsers, code executors, and external services

Reliability & Evaluation

• Implement guardrails, fallbacks, and human-in-the-loop checkpoints
• Design evaluation frameworks to measure agent accuracy, efficiency, and safety
• Monitor agent runs, debug failure modes, and improve prompt/tool reliability

Infrastructure & Deployment

• Deploy agents to production environments (cloud functions, containers, edge)
• Manage latency, cost, and token optimization across LLM calls
• Build observability tooling — logging, tracing, and alerting for agent behavior

Key Skills

Languages: Python (primary), TypeScript/JavaScript

AI/LLM: Prompt engineering, RAG, fine-tuning, LLM APIs (OpenAI, Anthropic, etc.), Agent Frameworks

Tools & Infra Docker, Kubernetes, vector DBs (Pinecone, Weaviate), message queues

Software Eng. APIs, async programming, system design, testing

Data: SQL, ETL pipelines, knowledge graphs

Benefits & Others

• Competitive benifits (salary, medical/dental insurance, 401K etc.)
• Hybrid work
• Latest technology stack - no legacy system