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Tier-1 Adobe and Bangalore metro increase competition, while senior 8–12 year specialization reduces applicant density.
High domain specificity in ML infrastructure and backend services reduces transferability across unrelated industries.
Explicit 8–12 years, required ML-infrastructure/backend experience and specific tech expectations make screening strict.
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Develop and scale AI infrastructure and core platform components powering intelligent experiences across Adobe Express products.
Build and improve backend services, data and inference pipelines, microservices, and workflows for ML model orchestration, inference, evaluation, and deployment.
Ensure reliability, observability, and performance of AI runtime systems including storage, caching, and data-access layers, collaborating with engineers, researchers, and product teams.
8 - 12 years of experience in software engineering, backend infrastructure, data systems, or ML infrastructure.
Strong knowledge of distributed systems, backend services, scalable system development, APIs, and data pipelines.
Experience working in cloud environments with production engineering and service deployment.
Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, or related technical field, or equivalent experience.
Experienced in building or supporting ML systems especially related to large language models (LLMs), model inference, orchestration, or evaluation.
Familiar with generative AI technologies, MLOps practices, and distributed data frameworks like Kafka, Spark, or Flink.
Skilled in developing high-performance, scalable AI platforms with a focus on operational excellence such as reliability, observability, and efficiency.