Associate tech lead, devops

We are looking for a DevOps Engineer to join the Digitalisation Centre of Excellence within DSM at LSEG. This role focuses on building, operating, and continuously improving the platforms, pipelines, and operational foundations that support data, analytics, machine learning, and AI systems in production.

This is a hands-on engineering role with a strong emphasis on AWS, Kubernetes/EKS, DevOps and platform reliability, including CI/CD, infrastructure-as-code, observability, security controls and incident readiness.

The role also enables MLOps and AI productionisation, working closely with Machine Learning Engineers and AI Engineers to ensure ML and AI systems are deployable, observable, secure and maintainable in production.

You will be part of a single Digitalisation organisation that owns its platforms end‑to‑end, rather than operating as a shared service or advisory function.

Our team operates on the Standard UK Shift - 12:30 PM to 9:30 PM (Sri Lankan Time) and hybrid workstyle (3 days in office) to support global business operations. Refer Hybrid Working | LSEG for more information on our ways of working.

Key Responsibilities

• DevOps, Platform Engineering & Production Reliability
• Design, build, and operate CI/CD pipelines supporting data, ML, and AI services.
• Implement and maintain infrastructure‑as‑code (e.g. Terraform, CloudFormation, CDK) for repeatable, auditable environments.
• Own environment management across development, test, and production.
• Establish and maintain observability across platforms and services, including logging, metrics, dashboards, and alerting.
• Support operational readiness, including runbooks, support models, incident response and post-incident improvements.
• Continuously improve platform reliability, security, and cost efficiency.
• Design and support AWS event-driven architectures using services such as Lambda, S3, SQS, API Gateway, DynamoDB, IAM, Secrets Manager and Step Functions where appropriate.
• Build and maintain operational tracking patterns for asynchronous workflows, including status tracking, reprocessing, failure recovery and traceability.
• Deploy, operate and optimise EKS workloads, including resource allocation, scaling, worker node considerations and troubleshooting.

MLOps & AI Production Enablement

• Work closely with ML Engineers and AI Engineers to productionise models and AI services.
• Support ML/AI workloads with:
• model packaging and deployment patterns
• versioning and artefact management
• monitoring hooks for performance, drift and operational health, with ML/AI engineers retaining ownership of model logic and behaviour.
• Help establish standard patterns for training pipelines, inference services, and retraining workflows.
• Ensure ML and AI systems integrate cleanly with CI/CD, observability, and security controls.
• Enable MLOps practices without owning data science, feature engineering or model logic.

Security, Governance & Reliability

• Embed security and compliance controls into CI/CD pipelines and infrastructure by default.
• Support auditability through consistent configuration, logging, and deployment practices.
• Ensure production systems meet LSEG standards for reliability, access control, and operational governance.
• Proactively identify and mitigate operational and platform risks.
• Implement security and governance controls in pipelines, including SAST/SCA, container scanning, infrastructure scanning, policy-as-code and cost checks.

Collaboration & Ways of Working

• Work closely with:
• Cloud Data Engineers
• Analytics Engineers
• Machine Learning Engineers
• AI Engineers
• Process Intelligence & Use Case Discovery (as needed)
• Collaborate in an Agile delivery environment, participating in planning, reviews, and retrospectives.
• Contribute to platform documentation, standards, and reusable templates.

How This Role Fits in the Team

This role is a core engineering capability within the Digitalisation team, not a shared or external service.

The DevOps/MLOps Engineer focuses on how systems run in production, while:

• ML/AI Engineers own model design and behaviour
• Data Engineers own data pipelines
• Analytics Engineers own consumption layers

Platform ownership and prioritisation are led by the Principal Engineering Manager – Data, AI/ML & Analytics.

What Success Looks Like

• CI/CD pipelines are reliable, fast, and easy to use across teams.
• Production systems are observable, stable, and well‑understood.
• ML and AI services move from development to production smoothly and predictably.
• Incidents are well-managed, recoverable, and used to drive platform improvements.
• Engineers spend less time fighting infrastructure and more time delivering value.
• Production workloads have clear dashboards, alerts, runbooks, ownership and recovery paths.

Required Skills & Experience

• 5-6 years of experience in DevOps or Platform Engineering roles.
• Experience designing and operating CI/CD pipelines, preferably using GitLab CI/CD, for backend, data, ML and containerised services.
• Practical experience with infrastructure-as-code, preferably Terraform, with exposure to Terragrunt or reusable environment orchestration patterns.
• Strong understanding of Linux, networking, and AWS cloud environments.
• Experience with monitoring, logging, tracing, dashboards and alerting for production systems, using tools such as OpenTelemetry, DataDog, Grafana/Prometheus, Sentry or PagerDuty where appropriate.
• Strong hands-on experience deploying, operating, and troubleshooting containerised workloads on Kubernetes, preferably AWS EKS.
• Experience managing EKS workloads, including deployment patterns, scaling, worker node sizing, resource management, and operational troubleshooting.
• Practical experience with AWS serverless and event-driven services, including Lambda, S3, SQS, API Gateway, IAM and Secrets Manager.
• Ability to collaborate effectively with ML and AI engineers on production workloads.

Desirable Skills

• Exposure to MLOps patterns, including model packaging, model/service deployment, artefact versioning, inference endpoints, monitoring hooks and retraining workflows.
• Support reusable deployment patterns for ML/AI workloads, including SageMaker endpoints, containerised inference services and integration with observability controls.
• Exposure to operating AI/LLM services in production, including AWS Bedrock or similar managed AI platforms.
• Familiarity with AWS SageMaker or similar ML platforms.
• Experience supporting data pipelines (Spark, batch/streaming jobs).
• Knowledge of secure cloud engineering patterns.
• Experience working in regulated or high‑reliability environments.
• Experience with tools such as Semgrep, Trivy, Checkov, Open Policy Agent, TFLint, Infracost, GuardDuty or equivalent.
• Experience with container registries and artefact repositories such as AWS ECR and Artifactory.
• Exposure to GitOps deployment patterns, Flux, Argo CD or equivalent.

Why Join Us

You will join a team delivering high-impact digitalisation capabilities across DSM, where reliability, security, and production excellence truly matter. This role offers hands‑on ownership of modern DevOps platforms, close collaboration with ML and AI engineers, and the opportunity to shape how data, analytics, ML and AI services operate in production within a complex Capital Markets environment.

You will have the opportunity to learn, contribute and grow within a collaborative engineering team that values practical delivery, reliability and continuous improvement!

If you enjoy building platforms that help engineers move faster and operate services with confidence, this is a great opportunity to make a visible impact!