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

Bachelor's or Master's 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

S YNECHRON'S 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 applicant's gender, gender identity, sexual orientation, race, ethnicity, disabled or veteran status, or any other characteristic protected by law.

Candidate Application Notice

GenAI/ML Ops SRE | Python & Multi-Cloud Cloud-Native