Junior Python Developer Jobs in Richland, WA (NOW HIRING)

Job Summary:
Pacific Northwest National Laboratory (PNNL) is a world-class research institution focused on scientific research and advanced engineering initiatives. The Lead DevOps/Platform Engineer will contribute to next-generation systems, applying expertise in scalable system design and AI/ML engineering to build mission-critical capabilities while mentoring junior team members.
Responsibilities:
• We are seeking a Lead DevOps/Platform Engineer to join PNNL's advanced AI engineering initiatives, contributing to next-generation systems spanning agentic AI platforms, large-scale data orchestration, and real-time intelligence processing.
• In this role, you'll apply your expertise in scalable system design and AI/ML engineering to build mission-critical capabilities while developing your technical leadership and establishing yourself as a key contributor to our engineering community.
• You're an accomplished engineer with strong foundations in DevOps, scalable system design, AI/ML development, and production software engineering.
• You're ready to take on increasing technical responsibility, leading components of complex systems while mentoring junior team members.
• You excel at translating technical requirements into working solutions, selecting appropriate approaches for challenging problems, and contributing meaningfully to technical direction and project success.
• AI-Native Systems & Platforms•
• Design and deploy scalable agentic AI systems with dynamic reasoning and decision-making capabilities
• Architect LLM orchestration frameworks using LangChain, LlamaIndex, and emerging agent platforms
• Build MLOps platforms spanning experiment tracking, model versioning, deployment, and governance
• Develop developer-focused tooling, adapters, and interfaces for AI-native frameworks
• Integrate multi-modal data sources (text, vision, structured/sensor data) into cohesive reasoning pipelines
• Scalable Infrastructure & Data Systems•
• Design microservices architectures coordinating across multiple domains and security enclaves
• Lead distributed system design processing data from hundreds of sources simultaneously
• Architect real-time streaming platforms handling terabytes per hour with event-driven architectures
• Build robust data pipelines for petabyte-scale ETL, data lake/mesh architectures, and real-time analytics
• Design container orchestration (Kubernetes) and CI/CD pipelines for classified and edge environments
• Mission-Critical Production Systems•
• Deploy AI systems in highly secure environments with resilient agent-to-agent communications
• Create monitoring and observability systems (logging, metrics, tracing) across secure enclaves
• Ensure compliance with ethical AI standards and security-first DevOps practices
• Build geospatial processing, time-series, and intelligence data fusion capabilities
• Technical Leadership•
• Lead a team of engineers to deliver on high risk / high impact ambiguous technical scope
• Drive technical strategy and architectural decisions across cross-functional teams
• Translate ambiguous requirements and cutting-edge research into actionable technical roadmaps
• Lead design discussions shaping team-wide engineering standards
• Mentor engineering teams and guide junior scientists/engineers
• Platform Architecture & Infrastructure Leadership•
• Expert-level proficiency in Python and at least one additional language (Go, C#/.NET, C++) with proven ability to establish infrastructure automation standards, architect scalable tooling platforms, and guide teams in developing sophisticated automation frameworks
• Mastery of Infrastructure as Code principles with deep expertise in Terraform, CloudFormation, Pulumi, or ARM templates and demonstrated ability to design enterprise-wide IaC strategies, module libraries, and governance frameworks that enable consistent and secure infrastructure deployment
• Proven track record of architecting and leading implementation of enterprise-grade CI/CD platforms with ability to define build/release strategies, establish deployment patterns, and drive continuous delivery adoption while designing internal developer platforms that abstract complexity and accelerate team velocity
• Expert proficiency with GitOps methodologies (ArgoCD, Flux), infrastructure testing frameworks (Terratest, InSpec), and policy-as-code (OPA, Sentinel) with strategic application of AI assist tools to drive team productivity, accelerate automation development, and optimize operational efficiency
• Cloud Architecture & Orchestration Expertise•
• Demonstrated expertise architecting and leading multi-cloud infrastructure strategies across AWS, Azure, and GCP with deep expertise in containerization and Kubernetes ecosystem including production-grade container platforms, custom operators, CRDs, and multi-cluster strategies at organizational scale
• Expert ability to architect sophisticated event-driven systems using cloud-native services (EventBridge, Event Grid, Pub/Sub, SNS/SQS) with advanced knowledge of service mesh architectures (Istio, Linkerd, Consul) and API gateway patterns for zero-trust networking and complex microservice environments
• Mastery of cloud and container networking including CNI design, custom ingress implementations, advanced load balancing, service discovery patterns, and network security policies with ability to troubleshoot complex distributed system networking issues
• Experience architecting edge computing solutions, hybrid cloud strategies, and secure enclave deployments with understanding of data sovereignty, latency optimization, and security requirements for geographically distributed infrastructure
• Reliability Engineering & Security Leadership•
• Proven ability to architect comprehensive observability platforms integrating metrics (Prometheus, Thanos, Cortex), distributed tracing (Jaeger, Tempo), and logging systems (ELK, Loki, Splunk) with deep expertise in SRE principles including SLO/SLI frameworks, error budgets, and incident management
• Expert implementation of security-first infrastructure including secrets management (Vault, AWS Secrets Manager, Azure Key Vault), automated vulnerability scanning, DevSecOps toolchains, and security policy enforcement across all infrastructure layers
• Strategic capability to design enterprise disaster recovery and business continuity strategies including multi-region architectures, automated backup systems, RPO/RTO optimization, and regular DR testing with advanced chaos engineering practices to systematically improve system resilience
• Deep understanding of compliance frameworks (SOC 2, HIPAA, FedRAMP, PCI-DSS, GDPR) with proven ability to implement automated compliance controls, audit logging, and infrastructure hardening standards that meet regulatory requirements
• MLOps & Data Platform Engineering•
• Expertise in architecting end-to-end MLOps platforms with proven ability to design and implement model lifecycle management infrastructure including experiment tracking (MLflow, Weights & Biases), model versioning, model registries, feature stores (Feast, Tecton), and automated ML pipeline orchestration supporting continuous training and deployment
• Deep expertise in building infrastructure for ML model serving and deployment including real-time inference APIs, batch prediction systems, A/B testing frameworks, model monitoring for drift detection, and automated model retraining pipelines with canary deployments and rollback capabilities
• Advanced knowledge of distributed ML training infrastructure including multi-GPU and multi-node training orchestration, resource scheduling, and optimization for frameworks like PyTorch, TensorFlow, and JAX on Kubernetes-based platforms (Kubeflow, Ray, Spark ML) with deep understanding of compute resource management and cost optimization
• Proven ability to architect cloud-native data platforms with expertise in ETL/ELT orchestration frameworks (Airflow, Prefect, Dagster, AWS Step Functions), production data storage systems (S3, Redshift, Databricks Delta Lake, PostgreSQL, MongoDB, Snowflake), and distributed data processing frameworks (Spark/Databricks, Kafka, Flink, Ray) supporting petabyte-scale data systems and real-time ML feature pipelines
• Technical Leadership & Strategic Impact•
• Exceptional problem-solving and troubleshooting abilities with proven track record of resolving complex infrastructure incidents spanning ML pipelines, data platforms, and distributed systems while leading incident response and root cause analysis, combined with outstanding communication skills to translate technical complexity into business impact for executive leadership and stakeholders
• Demonstrated ability to establish infrastructure and MLOps documentation standards, create comprehensive runbooks for ML system operations and DR procedures, develop technical training programs, and build knowledge sharing practices while mentoring and developing platform engineering teams through technical guidance and architecture reviews
• Proven capacity to lead multiple concurrent infrastructure and MLOps initiatives while maintaining production reliability for both traditional applications and ML systems, managing competing priorities, balancing technical debt, and establishing on-call practices, incident response frameworks, and blameless post-mortem processes that drive systemic improvements
• Strategic ability to balance immediate operational needs with long-term infrastructure and MLOps vision, evaluate emerging ML infrastructure technologies, drive platform modernization initiatives including ML democratization, and establish technical roadmaps that enable organizational scaling, AI/ML innovation, and operational excellence
• National Interest Project Examples •
• Detect and prevent smuggling of drugs and contraband at ports of entry [Link]
• Develop large data pipelines to thwart funding for terrorists, nuclear proliferators, drug cartels, and rogue leaders [Link]
• Applying big data solutions to national security problems [Link]
• Applying image classification for nuclear forensics analysis [Link]
• Develop capabilities for scalable geospatial analytics [Link]
• This position is based in Richland, WA requires an onsite presence Monday through Thursday, with Friday as required by business needs.
Qualifications:
Required:
• PhD and 3 years of Software Engineering experience -OR-
• MS/MA and 5 years of Software Engineering experience -OR-
• BS/BA and 7 years of Software Engineering experience -OR-
• AA and 16 years of Software Engineering experience in designing, architecting, programming, deploying, and automating software solutions in support of scientific research or consumer digital product development -OR-
• HS/GED and 18 years of Software Engineering experience in designing, architecting, programming, deploying, and automating software solutions in support of scientific research or consumer digital product development
• U.S. Citizenship
• Background Investigation: Applicants selected will be subject to a Federal background investigation and must meet eligibility requirements for access to classified matter in accordance with 10 CFR 710, Appendix B.
• Drug Testing: All Security Clearance positions are Testing Designated Positions, which means that the applicant selected for hire is subject to pre-employment drug testing, and post-employment random drug testing.
Preferred:
• Degree in computer science, software engineering, or related field
• Track record of architecting and operating large-scale infrastructure supporting significant user bases, high-volume transaction systems, petabyte-scale data platforms, or production ML systems serving millions of predictions
• Experience building and leading high-performing platform engineering, DevOps, or MLOps teams through hiring, mentoring, technical guidance, and career development
• Experience establishing infrastructure practices, platform strategies, MLOps frameworks, and DevOps transformation initiatives at organizational scale
• Background in mission-critical, regulated, or high-security environments (government, defense, financial services, healthcare) with understanding of compliance requirements for both traditional systems and ML/AI applications
• Demonstrated success leading complex, multi-team infrastructure and MLOps initiatives from architecture through production deployment, operational handoff, and continuous improvement
Company:
Pacific Northwest National Laboratory operates as a government research laboratory. Founded in 1965, the company is headquartered in Richland, USA, with a team of 5001-10000 employees. The company is currently Late Stage.