AWS DevOps Engineer with AI/ML

Job Title: AWS DevOps Engineer with AI/ML

Location: Plano, TX

Duration: 06 Month RTH

Job Description:

Must Have Skills:

AI/ML knowledge (2-3 years)

AWS

Observability tools - Grafana, Slunk, Dynatrace

Automation scripting languages - Bash, shell script, groovy

Nice to have skills:

Programming language - java

DEvOps skillset

Terraform

Key Responsibilities

• Design, develop, and deploy AI/ML models and agent-based systems that automate technology and platform workflows for the internal Deposits2.0 platform.

• Lead the integration of intelligent agents into operational processes to improve decision-making, workflow execution, and process optimization across engineering and operations.

• Build AI-assisted tooling that improves Infrastructure as Code (IaC) development, validation, and change management (examples: Terraform, Ansible, CloudFormation style patterns) across cloud and on-prem environments.

• Partner with DevOps and platform engineering teams to enhance CI/CD pipelines using AI/ML for signal detection, predictive analytics, and automated remediation.

• Develop AI-powered observability automation to monitor, analyze, and proactively manage application and infrastructure health for internal platform services.

• Automate alert triage, root cause analysis assistance, and incident response workflows using ML-driven techniques, with clear guardrails and measurable outcomes.

• Engineer or enable data pipelines and feature workflows needed to support model training, evaluation, and real-time or near-real-time inference use cases.

• Implement and operate MLOps capabilities (deployment patterns, monitoring, quality gates, rollback strategies, documentation) aligned to enterprise expectations for reliability and risk management.

• Collaborate with cross-functional teams (engineering, product, SRE, operations, architecture, controls) to identify high-value automation opportunities and deliver outcomes that can be adopted at scale.

• Continuously evaluate emerging AI/ML approaches and tooling, and translate them into practical, secure, and maintainable platform capabilities.

Required Qualifications (Minimum)

• Bachelor's or Master's degree in Computer Science, Engineering, or related field, or equivalent practical experience.

• 5+ years total engineering experience with 3+ years hands-on experience delivering AI/ML engineering solutions in production environments.

• Strong programming skills in Python; Java experience is a plus, especially for enterprise platform integration.

• Experience with ML frameworks such as PyTorch, TensorFlow, and scikit-learn, including model training and evaluation workflows.

• Hands-on experience building agent-based systems and integrating them into real operational or engineering workflows.

• Experience applying AI/ML to automate or improve Infrastructure as Code workflows (examples: generation assistance, validation, policy checks, drift detection, change risk scoring).

• Familiarity with observability fundamentals and toolsets (examples: Prometheus, Grafana, ELK stack) and experience automating operational workflows using AI/ML.

• Strong foundations in data structures, algorithms, machine learning, statistics, and software engineering best practices.

• Experience integrating AI capabilities into modern software development practices and supporting legacy modernization or code transformation initiatives.

• Strong communication and collaboration skills, with the ability to work effectively across engineering and operations stakeholders.

Preferred Qualifications

• • Depth in one or more areas such as large language models, NLP, knowledge graphs, reinforcement learning, ranking and recommendation, or time-series analysis.

• • Experience with retrieval-augmented generation patterns or tool-using agents, including multi-step workflows and structured evaluation approaches.

• • Experience with MLOps practices and concepts such as model registries, feature store patterns, CI/CD for ML, and model monitoring and drift detection.

• • Experience building production systems on public cloud platforms (examples: AWS, Azure, GCP) and operating containerized workloads (examples: Docker, Kubernetes).

• • Experience operating ML solutions in regulated enterprise environments, including producing controls-oriented documentation and supporting auditability expectations.

• • Experience with platform-scale operational automation, including incident response automation and measurable reductions in operational toil.