Director of Platform & Performance Engineering at Agiloft ensuring backend platform scalability and performance. Leading a backend engineering team to support AI-driven contract lifecycle management solutions.
Responsibilities
Build and lead a high-performing Core Platform Engineering team that:
Rapidly improves scalability, reliability, and observability of the core backend platform
Establishes systematic load testing, regression detection, and performance benchmarking practices
Drives measurable reductions in latency, error rates, and infrastructure cost per transaction
Leverages modern tooling, including AI-assisted analysis, to accelerate bottleneck identification and remediation
Partners closely with application engineering and Cloud Operations to ensure platform stability under real-world production load
Own and drive the performance, scalability, and reliability roadmap for the backend platform in close partnership with the CTO and engineering leaders
Prioritize performance and stability investments while balancing near-term delivery commitments.
Establish practical performance engineering practices, including load testing, benchmarking, and regression detection.
Proactively identify and eliminate performance bottlenecks to ensure fast, responsive user experiences at scale.
Design and execute realistic production-scale load testing scenarios that mirror customer behavior.
Establish performance regression gates in CI/CD pipelines.
Drive systematic root-cause analysis of production performance incidents.
Quantify and publish performance baselines and improvement targets.
Establish performance baselines and drive measurable improvements in throughput, latency, and resource efficiency.
Drive excellence in relational database architecture across MySQL and PostgreSQL environments in AWS (RDS/Aurora).
Lead database optimization strategies, indexing improvements, and schema evolution practices.
Partner with Cloud Operations to align infrastructure and data-layer scaling strategies.
Improve transactional integrity, concurrency handling, and data reliability at scale.
Lead query optimization initiatives based on real production workload analysis.
Identify and resolve lock contention, connection pool saturation, and concurrency bottlenecks.
Partner with Cloud Operations to right-size database infrastructure based on performance data.
Strengthen metrics instrumentation, structured logging and tracing across backend services.
Partner with SRE and Cloud Operations to enhance diagnostic capabilities and operational transparency.
Define measurable performance and reliability KPIs.
Ensure comprehensive instrumentation across critical backend paths.
Correlate load testing results with runtime metrics and tracing data.
Improve mean time to detect (MTTD) and mean time to resolve (MTTR) for performance incidents.
Engage directly in cross-team architectural design reviews, complex system decisions, and strategic refactoring initiatives.
Provide technical mentorship and elevate backend engineering standards.
Balance strategic direction-setting with practical, hands-on execution where needed.
Serve as a senior escalation point for complex backend performance or architectural challenges.
Clarify backend platform ownership boundaries in partnership with Product Engineering and Cloud Operations.
Recruit and develop a team of strong backend, database, and platform engineers.
Set clear expectations for engineering rigor, delivery accountability, and code quality.
Foster a culture of accountability, speed, craftsmanship, and continuous improvement.
Partner effectively with Product Engineering and Cloud Operations to ensure cohesive platform evolution.
Build a strong team capable of independently owning critical platform domains.
Influence engineering standards and platform practices across multiple application teams through collaboration and technical leadership.
Requirements
12+ years of backend engineering experience, including senior technical leadership roles.
Deep hands-on expertise with Java (JEE, Spring, or comparable enterprise frameworks).
Strong proficiency in Python for tooling, automation, or services development.
Strong understanding of distributed systems, concurrency, and scalable SaaS architectures
Experience designing and implementing load tests.
Experience using performance, observability, and scalability testing tools (Grafana, Jmeter, query EXPLAIN/ANALYZE, OpenTelemetry).
Strong expertise in performance optimization of relational database access patterns, including MySQL and PostgreSQL.
Experience evolving large-scale applications toward modular or microservices-based architectures.
Familiarity with containerization technologies (Docker, Kubernetes).
Experience using modern engineering tools, including AI-assisted development tools, to improve productivity and code quality.
Ability to apply AI tools pragmatically while maintaining architectural integrity and engineering standards.
Demonstrated ability to collaborate effectively across engineering, infrastructure, and product organizations.
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