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Protocol Intelligence
Data-driven signals on your job's competitivenessBroad mid-level ML/MLOps role in Bangalore at a known SaaS employer attracts many qualified applicants.
Specialized ML/MLOps expertise is transferable across industries but still requires domain-specific infrastructure experience.
Explicit 6+ years requirement plus mandatory MLOps and cloud/infrastructure skills tighten shortlisting significantly.
Job Description
Structured overview of role & requirementsAbout This Role
Own and operate end-to-end production machine learning systems including pipelines and services for model training, refresh, evaluation, inference, and lifecycle management.
Design and improve scalable, reliable ML infrastructure and platform primitives to enable faster, secure, cost-effective deployment and operation of ML models and AI agents.
Lead technical design, cross-team architectural influence, and mentor engineers while collaborating with Data Science and product teams to ensure model health, observability, and operational excellence.
Minimum Requirements
6+ years of industry experience building and operating production ML or data-intensive distributed systems with substantial end-to-end ownership.
Strong Python engineering skills and proven ability to design maintainable, testable ML services and pipelines.
Experience with MLOps tooling and practices, including experiment tracking, model versioning/registry, CI/CD, data/model validation, deployment strategies, rollback, and production monitoring.
Experience with distributed ML/data infrastructure such as Spark, Ray, Databricks, Kubernetes, AWS, or equivalent platforms.
Ideal Candidate Profile
Experienced in productionizing classical ML, deep learning/NLP, embedding/retrieval, or LLM/agent workflows at scale.
Demonstrates strong operational judgment with ability to diagnose and resolve incidents across data, models, infrastructure, and serving layers.
Capable of translating ambiguous product and Data Science requirements into pragmatic technical plans and leading them to completion with clear communication to technical and non-technical stakeholders.