Ai Engineer Jobs

About The Position

Requirements

• Bachelor's degree in Computer Science, Engineering, AI/ML, or a related field; or equivalent practical experience.
• 3+ years building and operating production software, including significant work on ML or AI infrastructure.
• Strong Python, including software engineering fundamentals — automated testing, code review, and designing code others will read and extend.
• Experience with the ML model lifecycle: pipelines that carry a model from training through evaluation, deployment, monitoring, and retraining.
• Kubernetes experience — deploying, scaling, and debugging containerized workloads on a major cloud provider (AWS preferred).
• Experience with CI/CD, GitOps-based delivery, and infrastructure automation.

Nice To Haves

• ML workflow orchestration and experiment tracking (for example Kubeflow, Argo Workflows, Airflow, MLflow, or Weights & Biases).
• GPU infrastructure at scale: scheduling, multi-tenancy and resource isolation, autoscaling, and inference optimization such as quantization, batching, or graph compilation (for example ONNX Runtime or TensorRT).
• Feature stores, data versioning, or dataset lineage tooling.
• Progressive delivery for models — canary, shadow, or A/B deployment and automated rollback.
• Model monitoring and drift detection, including automated retraining triggers.
• Model governance and reproducibility practices: lineage tracking, audit trails, and approval workflows.
• Kubernetes-native serverless and event-driven autoscaling (for example Knative or KEDA).
• Experience building APIs or shared libraries consumed by other engineering teams.
• Production experience in an additional systems language such as Go or Java.
• Fine-tuning foundation models, or building distributed training pipelines.
• Experience delivering software in a regulated environment (HIPAA, CLIA, GxP, SOC 2).

Responsibilities

• Build and maintain data, feature, and training pipelines for ML and LLM workloads — ingestion, transformation, fine-tuning, distributed training, and reproducible experiment execution with lineage tracked from dataset and code to resulting model.
• Implement automated evaluation and promotion gates — performance benchmarks, regression checks, and validation criteria that determine whether a model advances toward production.
• Automate the model lifecycle end to end through CI/CD and GitOps: packaging, promotion across environments, progressive rollout, and rollback.
• Build and operate production model-serving infrastructure for LLMs and predictive models, including inference optimization, autoscaling, and low-latency serving across multiple model formats and runtimes.
• Architect and manage GPU infrastructure — scheduling, autoscaling, resource isolation, and utilization efficiency for training and inference workloads.
• Instrument the platform and the models on it — structured logging, telemetry, drift detection, and cost tracking — and build the triggers and pipelines that close the loop into retraining and revalidation.
• Extend model, dataset, and artifact registries, metadata systems, and versioning so every deployed model has a traceable, auditable history.
• Build the developer-facing surface of the platform: APIs, SDK components, templates, documentation, and runbooks that make it self-service, backed by well-tested code and active participation in design and code review.
• Uphold company mission and values through accountability, innovation, integrity, quality, and teamwork.
• Maintain regular and reliable attendance.
• Act with an inclusion mindset and model these behaviors for the organization.

Benefits

• The base pay for this position is $61,300.00 – $122,700.00