GenAIML Ops SRE | Python & Multi-Cloud Cloud-Native

Job Summary
Synechron seeks an experienced AI Agentic Operations Site Reliability Engineer (SRE) to design deploy and operate scalable AI-enabled systems and agentic workflows. This role blends hands-on AI/ML deployment with traditional SRE disciplines to deliver reliable secure and cost-efficient platforms. The ideal candidate will lead cross-functional teams drive automation and observability for AI-driven solutions and uphold governance and security standards in line with enterprise requirements.

Software Requirements

Required Skills (Essential)

• Experience deploying and operating AI/ML solutions including agentic AI workflows and large-scale model deployments

• Proficiency in Python for automation data processing and orchestration; familiarity with other languages as needed

• Strong cloud experience (AWS Azure or GCP) with practical knowledge of security IAM networking and cost optimization

• Experience with containerization and orchestration (Docker Kubernetes)

• Proficiency with CI/CD pipelines and infrastructure as code (e.g. Jenkins GitHub Actions GitLab CI; Terraform as preferred)

• Strong observability and monitoring capabilities (Prometheus Grafana CloudWatch/Stackdriver)

• SRE practices: incident response post-incident reviews capacity planning reliability engineering

• Proficiency in version control systems (Git) and collaboration tools (GitHub GitLab Bitbucket)

• Security and governance awareness including data privacy and risk management

Preferred Skills

• Experience with MLOps tooling model monitoring and bias/safety considerations

• Familiarity with multi-cloud strategies (AWS/Azure/GCP)

• Experience with serverless architectures and cloud-native services

• Knowledge of data governance data lineage and regulatory compliance (e.g. GDPR/CCPA)

Overall Responsibilities

• Design implement and operate scalable AI-enabled platforms and agentic workflows with a focus on reliability and performance

• Drive automation observability and incident response across AI/ML deployments and production systems

• Collaborate with data scientists software engineers product managers and security teams to translate requirements into robust solutions

• Define and implement SRE practices runbooks alerting on-call processes and change management

• Optimize costs and resources while maintaining service-level objectives (SLOs) and availability targets

• Lead technical risk assessments capacity planning and disaster recovery planning for AI workloads

• Ensure governance data privacy and security controls are embedded in all AI initiatives

• Mentor and coach junior engineers promoting best practices in reliability automation and security

• Maintain and communicate architecture diagrams deployment procedures and governance artifacts

• Stay current with AI/ML trends and industry best practices driving continuous improvement

Technical Skills (By Category)

Programming Languages (Essential & Preferred)

• Essential: Python for automation and orchestration

• Preferred: Go Java or Bash for tooling and automation support

Cloud Technologies

• Essential: Core cloud concepts (compute storage network IAM security)

• Preferred: AWS Azure and/or GCP depth; multi-cloud operational experience; serverless architectures

Containerization & Orchestration

• Essential: Docker

• Preferred: Kubernetes (and Helm)

CI/CD & IaC

• Essential: CI/CD pipelines and version control (Git); infrastructure as code basics

• Preferred: Terraform CloudFormation GitHub Actions GitLab CI; automated release governance

Monitoring & Reliability

• Essential: Observability stacks (Prometheus Grafana CloudWatch/Stackdriver)

• Preferred: AIOps distributed tracing error rate dashboards SRE-based incident management

Security & Compliance

• Essential: Basic security practices for AI/ML deployments; data privacy awareness

• Preferred: PCI-DSS HIPAA or enterprise security certifications; secure model serving practices

AI Frameworks & Tooling

• Essential: Experience with AI/ML deployment and orchestration tools

• Preferred: MLOps platforms model monitoring bias detection and governance frameworks

Development Tools & Methodologies

• Essential: Git Agile/SCRUM practices collaboration tools (Jira/Confluence)

• Preferred: DevOps toolchains testing and release automation incident management tooling

Databases & Data Management

• Essential: SQL and data management basics; data ingestion for AI workloads

• Preferred: NoSQL data lineage data governance concepts

Experience Requirements

• 7 years in roles spanning AI/ML data engineering or DevOps with significant production exposure

• Demonstrated track record delivering reliable AI/ML deployments and/or reliability-focused projects

• Experience collaborating with cross-functional teams across locations

• Preference for experience with regulated industries governance and security controls

• Alternative pathways: strong portfolio of AI/ML production work relevant certifications or leadership in large-scale data/AI initiatives

Day-to-Day Activities

• Design and operate AI/ML deployment pipelines; implement reliability improvements

• Collaborate with data scientists engineers and product stakeholders to define requirements and success criteria

• Maintain runbooks deployment guides and incident response playbooks

• Monitor system health respond to alerts and perform post-incident analyses

• Lead on-call coverage for AI workloads and coordinate with global teams

• Mentor teammates and promote best practices in reliability and security

Qualifications

• Bachelors or Masters degree in Computer Science Data Science AI or related field

• Certifications in cloud platforms SRE or AI/ML domains are advantageous

Professional Competencies

• Strategic thinking and advanced problem-solving for complex AI/ML systems

• Clear communication and stakeholder management across technical and business teams

• Leadership and mentorship capabilities for cross-functional teams

• Adaptability to evolving AI technologies and regulatory landscapes

• Innovation mindset with a focus on scalable secure and reliable AI delivery

• Time management and prioritization in dynamic high-stakes environments

SYNECHRONS DIVERSITY & INCLUSION STATEMENT

Diversity & Inclusion are fundamental to our culture and Synechron is proud to be an equal opportunity workplace and is an affirmative action employer. Our Diversity Equity and Inclusion (DEI) initiative Same Difference is committed to fostering an inclusive culture promoting equality diversity and an environment that is respectful to all. We strongly believe that a diverse workforce helps build stronger successful businesses as a global company. We encourage applicants from across diverse backgrounds race ethnicities religion age marital status gender sexual orientations or disabilities to apply. We empower our global workforce by offering flexible workplace arrangements mentoring internal mobility learning and development programs and more.

All employment decisions at Synechron are based on business needs job requirements and individual qualifications without regard to the applicants gender gender identity sexual orientation race ethnicity disabled or veteran status or any other characteristic protected by law.

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