Sr. Manager, Cloud & AI Engineer

ROLE SUMMARY

Join Pfizer Digitals Commercial Creation Center & CDI organization (C4) to leverage cutting-edge technology for critical business decisions and enhance customer experiences for colleagues patients and physicians. Our team of engineering data science and AI professionals is at the forefront of Pfizers transformation into a digitally driven organization using data science and AI to change patients lives leading process and engineering innovations to advance AI and data science applications from prototypes and MVPs to full production.

As a Sr. Manager Cloud & AI Engineer you will own the cloud environments backend services CI/CD pipelinesLLMOpstooling and observability infrastructure that enable fast reliable and secure AI product delivery.

You will partner with AI engineers data scientists and the enterprise platform team to ensureDecisionIQsinfrastructure is production-grade scalable and compliant. This role is for someone who thrives at the intersection of cloud engineering backend development and AI/ML operations ensuring that the platform beneath the product is rock-solid.

ROLE RESPONSIBILITIES

1) Cloud Infrastructure & Backend Engineering

• Design build andoperatecloud-native environments on AWS/Azure forDecisionIQ: compute storage networking and security

• Implement Infrastructure as Code (Terraform Ansible Helm) for reproducible version-controlled environments

• Develop andmaintainbackend services APIs and data pipelines that powerDecisionIQsAI features

• Define and enforce architecture patterns: containerization (Kubernetes/Docker) secrets management identity and access management and security guardrails

• Optimizecloud resourceutilization cost and performance across allDecisionIQworkloads

2)LLMOps& AI/ML Operations

• Build and operateMLOps/LLMOpspipelines: experiment tracking model registry model deployment monitoring and rollback

• Deliver LLM-as-a-Service capabilities for the team: API key management usage optimization cost tracking and guardrails

• Operationalize AI workflows including data pipelines feature stores model packaging and inference serving

• Partner with data scientists and AI engineers to accelerate model-to-production cycles with standardized tooling (MLflowLangChainLangfuse SageMaker Bedrock)

3) CI/CD Testing & Observability

• Own the CI/CD strategy: design andmaintainautomated pipelines (GitHub Actions) for build test and deployment across all services and ML workloads

• Establish observability and reliability: implement monitoring (OpenTelemetry Prometheus Grafana ELK) define SLOs and drive operational excellence (DORA metrics MTTR)

• ChampionDevSecOps: embed security controls automated compliance checks and supply-chain integrity into all pipelines

• Support test automation integration: ensure CI/CD pipelines enforce quality gates coverage targets and release readiness criteria

4) Team Contribution & Engineering Maturity

• Contribute to a highly effective engineering team; mentor colleagues on cloud DevOps andLLMOpsbest practices

• Drive engineering maturity through design docs architecture decision records (ADRs)IaCreviews and secure coding practices

• Partner with enterprise Data and AI Platform teams to alignDecisionIQinfrastructure with corporate standards and shared services

• Stay current with emerging cloud and AI operations technologies; evaluate and adopt tools that improve delivery velocity and reliability

This role covers a broad spectrum of skills and we encourage you to apply even if you meet partially.

BASIC QUALIFICATIONS

• Bachelors orMasters degree in Computer Science Engineering or related field

• 7 years in platform engineering DevOps cloud infrastructure or backend engineering roles

• Strong hands-on experience with AWS or Azure; Kubernetes/Docker; Terraform/Ansible/Helm

• Proficiencyin Python and scripting (Bash); experience with backend service development and RESTful APIs

• Hands-on experience with CI/CD pipelines (GitHub Actions Jenkins) and Git-based workflows

• Hands-on experience withLLMOpsand ML frameworks (LangChainMLflowLangfuse) and cloud AI services (SageMaker Bedrock Azure AI Foundry)

• Expertisein observability and reliability tools (OpenTelemetry Prometheus Grafana ELK) and secrets management (Vault AWS Secrets Manager)

• Demonstrated experience withDevSecOpsand secure SDLC practices

• Familiarity with cloud-based analytics ecosystems (AWS Snowflake)

• Hands-on experience working in Agile teams processes and practices

• Highly self-motivated; strong English communication skills (written and verbal)

PREFERRED QUALIFICATIONS

• Advanced degree (MS/PhD) in Computer Science Engineering or related field

• Experience withMLOpsplatforms (KubeflowMLflow; model registry/monitoring) and modern data architectures (lakehouse distributed systems)

• Background in performance/security testing chaos engineering and SRE practices

• Experience in regulated/compliance-aware environments (GxP HIPAA SOC2)

• Experience with serverless computing (AWS Lambda) and event-driven architectures

• Certifications: AWS/Azure Professional CKA/CKADHashiCorpTerraform Security (CCSP)

• Familiarity with BI backend concepts (Star Schema Snowflake)

Please apply by sending your CV and a motivational letter in English.

Work Location Assignment:Hybrid