Lead Engineer, AI Workflow Automation

About Logikality

Logikality is building an AI-native mortgage automation platform for lenders, processors, title teams, and mortgage operations teams.

Our mission is to bring intelligence, speed, and trust into complex mortgage workflows such as document intake, classification, extraction, validation, title intelligence, income analysis, and loan onboarding. We are not building generic AI demos. We are building production-grade AI systems that help mortgage teams reduce manual effort, improve turnaround time, increase accuracy, and make lending operations more scalable.

We are looking for a hands-on Lead Engineer who can take ambiguous business workflows, convert them into practical AI-powered solutions, and ship them into real product and operational environments.

This is a high-ownership role for someone who enjoys building, experimenting, integrating, automating, and solving real business problems using AI.

Role Summary

As a Lead Engineer, AI Workflow Automation at Logikality, you will work closely with the CTO, product stakeholders, domain experts, and engineering team to design and build AI-driven workflows across our mortgage automation platform.

You will be expected to understand business processes, identify where AI can remove friction, design scalable solutions, and take them from concept to working prototype to production-ready implementation.

This role requires strong engineering fundamentals, practical GenAI experience, comfort with APIs and integrations, and the ability to work in a fast-moving startup environment where clarity is created through execution.

What You Will Work On

You will help build AI-powered capabilities across areas such as:

• AI agents for mortgage operations
• Human-in-the-loop review workflows
• RAG-based knowledge retrieval and reasoning
• Workflow automation for internal and customer-facing processes
• AI evaluation, quality checks, and governance mechanisms
• Integration with third-party mortgage platforms and internal systems 

Key Responsibilities

1. Convert Mortgage Workflows into AI-Driven Solutions

• Work with domain experts and leadership to understand current mortgage operations workflows 
• Break down ambiguous business problems into clear AI use cases 
• Identify opportunities where AI can improve speed, accuracy, cost, and operational scalability 
• Redesign manual or semi-manual workflows into AI-first workflows 
• Create practical solution designs that can move quickly from idea to implementation 

2. Build and Ship AI-Powered Systems

• Design, develop, test, and deploy AI-enabled workflow applications 
• Build backend services, APIs, orchestration logic, and automation flows 
• Work with LLMs, vision models, document AI tools, RAG pipelines, and agentic workflows 
• Integrate AI components with existing product modules and data pipelines 
• Move fast from prototype to production while maintaining quality and reliability 

3. Drive Engineering Execution

• Own end-to-end delivery of assigned AI workflow modules 
• Write clean, maintainable, production-grade code 
• Collaborate with junior engineers and guide them on implementation quality 
• Create reusable components, patterns, and internal engineering accelerators 
• Improve developer productivity through better tools, templates, and automation 

4. Ensure Quality, Reliability, and Governance

• Define quality checks for AI outputs, including accuracy, consistency, and explainability 
• Build evaluation mechanisms for prompts, models, workflows, and AI agents 
• Implement controls to reduce hallucination, incorrect extraction, and unreliable reasoning 
• Ensure proper handling of sensitive mortgage and customer data 
• Design solutions with auditability, traceability, and compliance in mind 

5. Influence Adoption Across the Company

• Work with product, operations, leadership, and customer-facing teams to ensure solutions are usable 
• Translate business needs into technical execution plans 
• Communicate trade-offs clearly across accuracy, speed, cost, and complexity 
• Help create a culture where AI is applied thoughtfully, not superficially 

What Success Looks Like in the First 6 to 9 Months

• Several AI-powered mortgage workflows are live or ready for customer-facing use 
• Manual effort is measurably reduced in selected workflows 
• AI output quality is tracked through clear evaluation methods 
• Reusable patterns are created for document AI, RAG, agents, and workflow automation 
• Junior engineers are able to contribute faster because of your technical guidance 
• Logikality has a stronger foundation for scalable, reliable AI product development 

Required Credentials

• An Engineering degree is must
• 3 to 8 years of software engineering experience
• Strong hands-on experience building backend systems, APIs, workflow automation, or AI applications 
• Practical experience with GenAI tools, LLMs, or AI-powered automation in real use cases 
• Strong programming experience in Python or a similar backend language 
• Experience building production-grade systems, not just experiments or demos 
• Good understanding of APIs, databases, backend architecture, and system integrations 
• Ability to work directly with business stakeholders and convert requirements into working software 
• Comfort operating in ambiguity and creating structure through execution 

Technical Skills We Expect (Must Have)

• Strong hands-on coding ability, preferably in Python 
• Experience with LLMs such as OpenAI, Claude, Gemini, or open-source models 
• Understanding of prompt design, structured outputs, function calling, or tool use 
• Experience with document processing, NLP, OCR, vision models, or extraction workflows 
• Ability to build APIs and backend services 
• Experience integrating third-party systems or internal services 
• Understanding of RAG pipelines, AI agents, or orchestration patterns 
• Exposure to workflow automation tools or frameworks such as n8n, Temporal, Airflow, LangGraph, CrewAI, or similar
• Experience with AWS, GCP, or Azure
• Exposure to mortgage, lending, fintech, document automation, or compliance-heavy domains
• Understanding of MLOps or LLMOps concepts such as model monitoring, evaluation, versioning, and deployment 
• Experience with FastAPI, PostgreSQL, Docker, queues, or event-driven architecture 
• Experience mentoring junior engineers 
• Familiarity with human-in-the-loop workflows and review systems