Senior AI / ML Engineer at Dolby – Bangalore | Jobyssey

Join the leader in entertainment innovation and help us design the future. At Dolby, science meets art, and high tech means more than computer code. As a member of the Dolby team, you’ll see and hear the results of your work everywhere, from movie theaters to smartphones. We continue to revolutionize how people create, deliver, and enjoy entertainment worldwide. To do that, we need the absolute best talent. We’re big enough to give you all the resources you need, and small enough so you can make a real difference and earn recognition for your work. We offer a collegial culture, challenging projects, and excellent compensation and benefits, not to mention a Flex Work approach that is truly flexible to support where, when, and how you do your best work.

At Dolby, we’re changing the way the world experiences sight and sound. We enable people to experience music and movies; videos and pictures in all its intended grandeur and make life & work more meaningful and immersive. We give technology to the world’s content creators, owners, and distributors; manufacturers of TV, Mobile, and PC; and social and media platforms; so that they can truly delight their customers. We're the ones behind the astounding sound and sight experiences in the movie theaters and in your living room; on your mobile phones and on the internet.

About the Role

Dolby's Data & AI Platform team builds the systems that powers machine learning and analytics across the company, from codec telemetry spanning billions of devices to the media pipelines behind Dolby Atmos and Dolby Vision. Our data is high-volume, real-time, and deeply tied to how people experience sound and image worldwide.

We're hiring a Senior AI Engineer to design and own the data pipelines, platform services, and ML systems that our researchers, data scientists, and product teams depend on daily. You'll work across the full lifecycle, from ingestion through model deployment on systems that run at scale in production Kubernetes and Databricks environments.
This is a hands-on engineering role with real ownership. You'll build things, put them in production, and keep them running.

What You'll Do

Build and operate ML-ready data systems. Create the data preparation, feature generation, and training pipelines that AI researchers and ML engineers use to take models from experiment to production. Own data versioning, validation, and reproducibility for ML workflows.

Deploy and support production AI/ML systems. Build and maintain the pipelines that serve model training, testing, validation, deployment, and inference in production. Partner with ML engineers to operationalize models reliably.

Own data systems end-to-end. Design, build, and operate ETL/ELT pipelines that ingest data from real-time event streams, third-party APIs, and media-rich sources into our lakehouse architecture. Own throughput, latency, reliability, and cost.

Run production workloads on Kubernetes. Deploy, monitor, and troubleshoot containerized data services and distributed processing applications in cloud-native Kubernetes environments.

Develop platform and data products. Build SDKs, APIs, and reusable frameworks that make it easy for other engineering and research teams to access data and adopt the platform.

Ensure data quality and governance. Implement validation, reconciliation, and monitoring processes. Maintain data catalogs and metadata so teams can discover, trust, and reuse data assets across the organization.

Improve observability and operational health. Design monitoring, alerting, and logging for pipelines and infrastructure. You'll be expected to catch problems before users do.

What You Bring

Required

5+ years in data engineering, data platform engineering, AI engineering or a closely related field.
Expert-level proficiency in at least one major programming language such as Python, Scala, or Java.
Production ML/AI pipelines: you have personally built or co-built pipelines that took models through training, testing, validation, and deployment into production.
Databricks: hands-on production experience with Lakehouse architecture, Delta Lake, Spark optimization, and Workflows. Experience managing large-scale, heterogeneous datasets on Databricks.
Ray / Apache Spark (or equivalent distributed processing framework): deep, production-scale experience building and optimizing data pipelines.
Kubernetes: hands-on experience deploying, operating, and troubleshooting production workloads - not just deploying onto clusters, but understanding how they run.
Cloud platforms: strong experience with AWS, GCP, or Azure and their data services.
SQL Mastery: Advanced SQL skills for data manipulation, analysis, and optimization.
Database Knowledge: Solid understanding of relational and NoSQL databases.
Data Architecture: solid understanding of data modeling, schema design, and lake/lakehouse/warehouse patterns.
Event-driven systems: Experience with messaging, pub/sub, queues, or streaming platforms.

Preferred

Experience with MLOps tooling (MLflow, Airflow, Kubeflow, Feast, or similar).
Familiarity with feature stores, vector databases, embedding pipelines, or retrieval systems.
Experience on batch and online inference workloads with an understanding of latency sensitive environments.
Experience supporting generative AI, LLM, or multimodal AI workloads.
Optimized GPU utilization during model training or fine-tuning.
Understanding of distributed computing fundamentals - concurrency, consistency and fault tolerance.
CI/CD practices and Infrastructure-as-Code.
Contributions to open-source data or AI projects.

Education

Master’s or Bachelor's degree in Computer Science, Engineering, Data Science, or a related field, or equivalent professional experience.

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