Senior/Lead - Backend Engineer/Data Engineer - AI Engineering

This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Senior/Lead Backend Engineer – Data Engineer / AI Engineering in the United States.

This role sits at the convergence of backend engineering, large-scale data systems, and applied AI innovation, supporting mission-critical analytics and decisioning platforms. You will design and build highly scalable backend architectures and data pipelines that power real-time intelligence across fraud detection, automation, and optimization use cases. Working within a high-performing AI engineering environment, you will help integrate LLM-driven capabilities into production-grade systems while ensuring reliability, governance, and performance at scale. The position requires close collaboration with data scientists, ML engineers, and product teams to deliver robust, AI-enabled services. You will also contribute to architectural strategy, shaping systems that handle high-volume, low-latency workloads across global applications. This is a hands-on technical leadership role where engineering excellence directly enables impactful, data-driven decision-making at enterprise scale.

• Design, build, and maintain scalable backend systems and data pipelines supporting AI-driven analytics and decisioning platforms.
• Develop high-throughput data ingestion, transformation, and storage solutions for real-time and batch processing workloads.
• Implement AI-powered services, including LLM-based solutions for fraud detection, decision automation, and process optimization.
• Design and optimize Retrieval-Augmented Generation (RAG) architectures and prompting strategies for mission-critical applications.
• Collaborate with cross-functional teams to develop APIs and microservices enabling intelligent, data-driven workflows.
• Build and optimize distributed systems to support low-latency, high-volume data processing at scale.
• Implement robust monitoring, testing, and observability frameworks to ensure system reliability, performance, and security.
• Define and evolve backend and data architecture standards to support enterprise-scale AI and analytics platforms.
• Mentor and guide engineering team members, promoting best practices in backend development, data engineering, and AI integration.

Requirements:

• 7+ years of experience in backend engineering, data engineering, or building large-scale production systems.
• Strong programming skills in Python, Java, Go, or similar languages, with emphasis on clean and maintainable code.
• Hands-on experience with distributed data frameworks such as Apache Spark, Kafka, or Hadoop.
• Strong knowledge of relational and NoSQL databases and data modeling principles.
• Experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies like Docker and Kubernetes.
• Proven ability to design and operate scalable, high-performance backend systems in production environments.
• Experience with observability, testing strategies, performance tuning, and A/B testing methodologies.
• Hands-on exposure to AI/ML system integration, including working with Large Language Models (LLMs).
• Familiarity with Retrieval-Augmented Generation (RAG) and vector databases (e.g., Pinecone, Weaviate, pgvector) is a plus.
• Strong communication and collaboration skills, with experience mentoring engineers and influencing technical direction.
• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field preferred.

Benefits:

• Competitive base salary ranging from $140,000 to $220,000 USD annually, depending on experience and location.
• Comprehensive benefits and rewards package designed to support health, financial, and personal well-being.
• Flexible remote work environment with strong emphasis on work-life balance.
• Opportunity to work on high-impact AI and data-driven products used at global scale.
• Career growth within a leading organization in analytics, AI, and decisioning technologies.
• Inclusive, collaborative culture that values ownership, innovation, and continuous learning.
• Access to advanced technical challenges in large-scale distributed systems and AI engineering.
• Learning and development opportunities to strengthen expertise in AI, backend systems, and data platforms.