Senior/Lead Backend/Data Engineer - AI Engineering
New
USFull-TimeLead
Salary140,000 - 220,000 USD per year
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Job Details
- Experience
- 7+ years
- Required Skills
- AWSPythonGCPHadoopJavaKafkaAzureGoSpark
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.
Responsibilities
- 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.
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