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
SYNECHRON’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
