Lead Software Engineer

Lead Software Engineer – AI-Driven Development & Agentic Workflows

Who We Are

Solera is a global leader in data and software services, transforming every touchpoint of the vehicle lifecycle into a connected digital experience. Solera processes over 300 million digital transactions annually for approximately 235,000 partners and customers in more than 90 countries. Our teams work on mission‑critical platforms that demand reliability, scalability, and thoughtful evolution of complex systems.

We’re looking for a hands-on Lead Software Engineer who can help design, build, and deliver production-grade software with a strong focus on AI-driven development, agentic AI systems, and workflow automation. You will work as a senior technical contributor and workstream lead, writing production code regularly while guiding engineers through complex implementation decisions.

This role is ideal for an experienced engineer who:

Builds production software using modern engineering practices and AI-assisted development tools

Designs and implements agentic AI workflows that connect LLMs, tools, APIs, data sources, and business processes

Leads delivery for features, services, and workflow automation capabilities within a team or product area

Applies pragmatic architecture patterns that improve reliability, scalability, maintainability, and developer velocity

Mentors engineers through hands-on pairing, code review, technical design, and adoption of AI-driven development practices

You will operate with meaningful autonomy within your team, influence implementation patterns across related services, and be known as someone who ships high-quality software, unblocks delivery, and helps the team adopt practical AI-enabled engineering workflows.

WHAT YOU’LL DO

Build, Ship, and Own

Write production-quality code regularly across services, APIs, workflow engines, and AI-enabled capabilities

Lead delivery of high-impact features from design through deployment and production support

Modernize legacy components incrementally using pragmatic, low-risk migration patterns

Design, build, and evolve scalable microservices, APIs, integrations, and event-driven workflows

Translate business and product requirements into reliable software designs, implementation plans, and working solutions

Identify implementation risks early and drive practical solutions that keep delivery moving without sacrificing quality

AI-Driven Development, Agentic AI & Workflow Automation

Design and implement AI-enabled workflows using large language models, retrieval patterns, structured outputs, and tool/function calling

Build agentic systems that can execute multi-step workflows, invoke tools, call APIs, maintain state, and support human-in-the-loop reviews where appropriate

Implement MCP-style or equivalent orchestration patterns for context management, tool access, memory, permissions, and workflow execution

Integrate AI workflows with enterprise data sources, business applications, APIs, queues, and operational systems

Apply grounding, validation, guardrails, prompt/version management, and evaluation techniques to reduce hallucinations and improve reliability

Build monitoring, observability, feedback loops, and quality checks for production AI behavior

Partner with product, security, architecture, and operations teams to ensure AI solutions meet business, privacy, compliance, and supportability requirements

Technical Leadership & Delivery

Lead technical execution for a team, feature area, or workstream while remaining hands-on in the codebase

Contribute to architecture decisions for service boundaries, integration patterns, data ownership, and AI workflow design

Make sound engineering tradeoffs across delivery speed, reliability, scalability, security, cost, and maintainability

Serve as a technical escalation point for implementation challenges involving distributed systems, AI workflows, data integrations, and production issues

Create reusable patterns, examples, and guidance that help engineers deliver consistent, high-quality solutions

AI‑Assisted Engineering & Developer Productivity

Use AI-powered development tools such as GitHub Copilot, ChatGPT, Claude, or equivalent tools to accelerate coding, refactoring, testing, debugging, and documentation

Establish practical team practices for safe, reviewable, and high-quality AI-assisted development

Create prompts, reusable workflows, coding patterns, and automation scripts that improve developer productivity

Help engineers adopt AI-driven development without weakening code review discipline, testing standards, security practices, or production ownership

Mentorship & Team Elevation

Mentor engineers through pairing, code reviews, design discussions, and implementation planning

Help engineers build stronger judgment around AI-enabled development, workflow automation, testing, and production readiness

Coach less-experienced developers on modern engineering practices, secure AI usage, and maintainable system design

Promote a culture of ownership, learning, collaboration, and technical accountability

Lead by example with clear communication, humility, urgency, and high engineering standards

Technical Execution & Operations

Build and maintain SaaS applications using modern frameworks and cloud platforms

Design and implement RESTful APIs and event-driven integrations

Work with relational and NoSQL databases, optimizing for performance and reliability

Build containerized applications using Docker and deploy via Kubernetes

Partner with DevOps/SRE to ensure strong CI/CD pipelines, observability, and safe deployments

Participate fully in the SDLC: design, coding, testing, deployment, and production support

REQUIRED QUALIFICATIONS

7+ years of professional software development experience

Proven experience leading delivery of complex features, services, or technical workstreams

Hands-on experience designing, building, and operating production software systems

Experience applying AI-assisted development tools in real engineering workflows

Hands-on exposure to LLM-enabled applications, agentic workflows, automation systems, or AI-integrated product capabilities

Demonstrated ability to mentor engineers and improve team execution through practical technical leadership

Technical Skills

Strong proficiency in C# and .NET, including ASP.NET Core and modern .NET development

Frontend framework experience such as React, Angular

Strong understanding of RESTful API design, distributed systems, and production service development

Experience building microservices, integration services, and event-driven workflows

Hands-on experience with LLMs, AI workflow orchestration, or agentic AI patterns, including tool/function calling, context management, structured outputs, and workflow state

Experience integrating AI-enabled systems with databases, APIs, queues, and enterprise applications

Hands-on experience with relational databases such as SQL Server or PostgreSQL

Working knowledge of NoSQL data stores, caching strategies, and performance optimization

Experience with Docker, containerized application development, and cloud deployment practices

Practical experience with Kubernetes or container orchestration platforms

Comfort working in cloud environments such as Azure or AWS

Proficient with Git, pull requests, code reviews, branching strategies, and modern CI/CD workflows

Strong understanding of automated testing, observability, secure coding, and production support practices

Must Have Skills:

Strong proficiency in C# and .NET, including ASP.NET Core and modern .NET development

Frontend framework experience such as React(Preferable)

, Angular

Hands-on experience with relational databases such as SQL Server

Hands-on experience with LLMs , AI workflow orchestration, or agentic AI patterns, including tool/function calling, context management, structured outputs, and workflow state

Develop production-quality code across distributed services and architecture , leveraging expertise in APIs, workflow orchestration, and AI-enabled solutions.

Experience with Python, Java, TypeScript, or polyglot engineering environments

Experience with message queues, event streaming, or workflow orchestration platforms

Experience with vector databases, retrieval-augmented generation, knowledge graphs, or semantic search

Experience building AI evaluation harnesses, prompt/version management, safety checks, or governance workflows

Background with high-throughput, real-time, or operationally critical SaaS systems

Strong background in Agile/Scrum environments and cross-functional delivery

Bachelor’s degree in computer science or equivalent practical experience

WHAT SUCCESS LOOKS LIKE

You consistently deliver high-quality features, services, and AI-enabled workflow capabilities

Agentic AI workflows you build are reliable, observable, secure, and useful in real business processes

The team moves faster because AI-assisted development practices are applied safely and effectively

Legacy components are improved incrementally without creating unnecessary delivery risk

Engineers rely on you for implementation guidance, design judgment, and practical problem solving

Code quality, test coverage, operational readiness, and delivery predictability improve measurably

You are recognized as a hands-on technical leader who ships, mentors, and raises the team’s engineering bar