Lead Machine Learning Engineer / AI Systems Architect

About the role

Accenture Industry X is looking for a Lead Machine Learning Engineer / AI Systems Architect to design and scale enterprise‑grade vehicle connectivity data platforms. The role combines predictive modeling, neural networks, generative and agentic AI into cloud‑native services that monitor fleet quality and operations.

Key responsibilities

• Architect and implement scalable, fault‑tolerant AI/ML systems on cloud or hybrid environments.
• Design end‑to‑end machine‑learning pipelines from data ingestion to real‑time inference and continuous evaluation.
• Develop and maintain microservice APIs, containerized workloads, and infrastructure‑as‑code using Terraform or Pulumi.
• Implement CI/CD, monitoring, and observability stacks (Prometheus, Grafana, OpenTelemetry) to ensure reliable deployments.
• Lead MLOps practices including experiment tracking, model versioning, automated retraining, and performance monitoring with tools such as MLflow, Kubeflow, DVC, SageMaker, Vertex AI or Azure ML.

Required profile

• Expert‑level proficiency in Python and solid experience with Java, Go or TypeScript.
• Strong background in distributed systems, API design, and microservice architecture.
• Proven track record of building production‑grade AI solutions at scale.
• Hands‑on experience with cloud platforms (AWS, Azure, GCP) and container orchestration (Kubernetes, Docker).
• Ability to balance innovation with operational reliability and cost optimisation.

Required skills

• Python, Java, Go, TypeScript
• Machine learning algorithms (regression, boosting, SVM, CNN, RNN, Transformers)
• Feature engineering, XAI, reinforcement learning, causal inference, graph analytics
• MLOps tools: MLflow, Kubeflow, DVC, SageMaker, Vertex AI, Azure ML
• CI/CD, Terraform, Pulumi
• Monitoring: Prometheus, Grafana, OpenTelemetry
• Cloud services: AWS, Azure, GCP
• Containers: Docker, Kubernetes