Sr. Manager, Cloud & AI Engineer

ROLE SUMMARY

Join Pfizer Digital’s 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 Pfizer’s 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 pipelines,LLMOpstooling, 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 ensureDecisionIQ'sinfrastructure 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 powerDecisionIQ'sAI 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 (MLflow,LangChain,Langfuse, 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

• Bachelor's orMaster's 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 (LangChain,MLflow,Langfuse) 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 (Kubeflow,MLflow; 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/CKAD,HashiCorpTerraform, 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