Description
Must-Have
- Strong foundation in ML inference, deployment, and quality testing
- Demonstrated ability to ramp up quickly on new and unfamiliar tech stacks — this is the single most important trait
- End-to-end problem-solving mindset — can own a problem from model handoff to user-facing behavior
- Core ML knowledge sufficient to benchmark models and collaborate with researchers
- Experience deploying models in cloud environments, ideally GCP
- Ability to work with ambiguity and unknowns
Good to Have
- Exposure to Java or JVM-based systems (model integration happens in Java; deep expertise not required)
- Familiarity with streaming data architectures
- Experience in hybrid cloud/on-prem environments
- Large scale systems and someone who has worked 0 -> 1 is a plus
Strong system design skills expected at both levels – Tech-lead caliber.
Requirements
You will own the end-to-end ML model lifecycle from post-training through production — everything after the researchers hand off a trained model. This is not a research role. You are the engineer who takes models and makes them real: benchmarked, deployed, monitored, and integrated into live production applications. You will work directly with ML researchers, production engineers, and platform teams in a fast-moving hybrid cloud environment.
Job responsibilities
Inference & Deployment
- Evaluate and benchmark new ML inference frameworks to guide production decisions
- Deploy models to GCP and integrate them into production applications and Java-based streaming pipelines
- Own deployment automation end-to-end — from model handoff through live serving
- Monitor how models behave in production for real end-users
Performance & Quality
- Design and execute benchmarking, performance testing, and quality testing on ML models
- Perform model sampling to support quality evaluation and researcher feedback loops
- Debug issues across the full stack — from inference layer down to streaming pipeline
Cross-functional Collaboration
- Partner with ML researchers to provide benchmarking feedback and guide inference decisions — requires enough core ML knowledge to have a meaningful technical handshake
- Adapt rapidly to non-standard and evolving tech stacks across hybrid (on-prem + GCP) infrastructure
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What we offer
Why work at GlobalLogic
Our goal is to build an inclusive positive culture where everyone can feel comfortable being themselves, empowering our people to create their own high standards and therefore more value. We work together to promote fairness while recognising, valuing and embracing differences – providing a transparent support structure and generous training budget to help our people develop skills to progress their career. Our region also supports a hybrid model which can flex across a wide spectrum of working options determined by our business, customer and individual needs.
You’ll benefit from a comprehensive health and wellness plan, private healthcare (clinical and mental wellbeing), and discounted gym memberships. We offer a fantastic benefits package including a competitive pension scheme and recognition schemes through bonus/reward initiatives. Colleagues are entitled to an annual volunteering day – so you can take time to support a cause close to your heart. We also love to stay social at our trips to the zoo, quiz nights, sports events, theatre trips and much more.
We are an equal opportunities employer. It is our policy to promote an environment free from discrimination, harassment and victimisation.
About GlobalLogic
GlobalLogic, a Hitachi Group Company, is a trusted digital engineering partner to the world’s largest and most forward-thinking companies. Since 2000, we’ve been at the forefront of the digital revolution – helping create some of the most innovative and widely used digital products and experiences. Today we continue to collaborate with clients in transforming businesses and redefining industries through intelligent products, platforms, and services.
