Juniper Square - Senior Technical Lead (Full Stack)
Requirements
• This role will lead the design and delivery of end-to-end systems—from user-facing applications to backend services and cloud infrastructure. You will guide engineering teams, drive architectural decisions, and ensure best practices across the full software development lifecycle. • Experience leveraging AI tools and building AI-driven solutions will be a significant advantage, especially for developing intelligent features, automation, and data-driven capabilities within products. • Bachelor’s or Master’s degree in Computer Science, Engineering, or related field. • 10–12 years of software engineering experience, with at least 3+ years in a technical leadership role. • Strong expertise in: • Python (FastAPI preferred) OR Java • ReactJS and TypeScript • REST and GraphQL API development • Asynchronous processing (Celery or similar) • AWS cloud services • Docker, Kubernetes, and containerization • Solid understanding of: • Microservices architectures • Distributed systems design • SQL and NoSQL databases • CI/CD pipelines • Monitoring and observability frameworks • Experience building AI-driven applications or integrating AI capabilities into products. • Experience with event-driven systems such as Kafka, SNS/SQS. • Exposure to Domain-Driven Design (DDD) and clean architecture principles. • Knowledge of Infrastructure as Code (Terraform or CloudFormation). • Experience with performance tuning, load testing, and AWS cost optimization. • Strong leadership, mentoring, and stakeholder management abilities. • Excellent communication and documentation skills. • Analytical mindset with strong problem-solving capabilities. • Ability to operate effectively in a fast-paced, agile environment.
Responsibilities
• Own Technical Direction and Architecture • Set the technical direction for AI systems, including shared AI SDKs, guardrails, evaluation frameworks, feedback systems, and agentic workflow infrastructure • Own architecture and technical strategy for complex backend and AI platform systems, from design through production • Lead technical design for ambiguous, cross functional initiatives, evaluating tradeoffs, aligning stakeholders, and driving implementation • Evaluate and select technologies with a bias toward what ships well and scales sustainably • Build and Operate AI Systems • Write production code as a hands on individual contributor, this is not a role that delegates implementation to others • Design and operate LLM powered systems, including RAG pipelines, agentic workflows, evaluation infrastructure, guardrails, and model observability • Own end to end reliability of AI systems, from design through structured output delivery • Define quality benchmarks • Champion AI Native Development • Champion and embed AI native development practices and tools, such as Cursor and Augment, to drive meaningful productivity gains across the team • Foster a culture of rapid iteration, high velocity, and quality, including guiding the effective use of AI code generation • Bring strong, informed opinions on how to get the most from AI assisted development while maintaining reliability and correctness • Lead and Grow the Team • Mentor engineers, raise the quality of technical decision making, and help the team execute with consistency • Establish coding standards, review practices, and architectural documentation that scale as the team grows • Help define what “good” looks like for a team building at speed without sacrificing quality • Partner with recruiting to build and grow the team • Collaborate Cross Functionally • Work closely with Engineering Managers, Product, Design, and QA to translate requirements into executable technical plans • Participate actively in design reviews and roadmap discussions with a grounded, implementation level perspective • Handle most cross team conflicts and technical decisions autonomously • Ability to critically evaluate AI generated code and outputs, including identifying failure modes, regressions, and edge cases introduced by AI assisted development • Experience building and shipping production grade software using AI assisted workflows across the full SDLC • Hands on experience developing or integrating LLM powered systems, such as agents, copilots, tool using workflows, or multi step reasoning systems • Familiarity with patterns such as tool calling agents, planning and execution loops, and retrieval augmented generation (RAG) • Demonstrated ability to leverage modern AI tools to improve development velocity, code quality, and problem solving • Experience contributing to AI powered features, such as intelligent search, conversational interfaces, recommendations, and automation • Working knowledge of LLMs, embeddings, semantic search, and RAG pipelines • Ability to identify and evaluate opportunities to integrate AI capabilities into products and workflows
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