Top Senior Python Jobs

Build and operate scalable data pipelines and lakehouse architectures using Python, Databricks, Spark, Delta Lake, and AWS. Responsibilities include batch and streaming ingestion, CDC, workflow orchestration, testing, governance, security, performance and cost optimization, CI/CD, infrastructure as code, monitoring, incident response, and documentation. The role also requires responsible use of enterprise AI-assisted development tools and collaboration within an agile software engineering team.

Leads software engineering for network automation platforms, delivering secure, scalable, and production-ready solutions. Responsibilities include designing and developing Python and React applications, building microservices and API integrations, troubleshooting systems, reviewing code, improving automation and operational stability, and promoting responsible AI-assisted engineering practices. The role also involves evaluating vendor and internal architectural solutions, guiding agile development, and partnering with stakeholders to deliver sustainable technology outcomes.

Designs, develops, supports, and troubleshoots complex software and platform solutions using Python, Slurm, Spark, Linux, and server infrastructure. Leads technical problem-solving, documentation, SDLC practices, and architecture-related initiatives while mentoring an offshore team and guiding colleagues. The role requires strong application or platform support experience, project or team leadership, and collaboration across QA and production environments.

Provides principal-level technical leadership for a regulatory compliance and risk management platform. Leads architecture and engineering strategy across data ingestion, metadata, search, audit capabilities, integrations, scalability, resiliency, observability, and operational efficiency. Advises leadership, resolves complex enterprise challenges, modernizes applications, defines technical standards, and mentors engineers while delivering reliable, traceable, production-ready solutions.

Build and operate agentic AI evaluation harnesses, provenance controls, and CI/CD integrations for machine-authored code changes. Develop automation services and test tooling using Java, Python, Shell, and related technologies; implement quality gates, audit evidence, signed attestations, rollback controls, observability, and operational support. Lead responsible adoption of AI-assisted engineering practices while validating generated outputs for correctness, security, reliability, and compliance.

Leads software engineering within an agile corporate technology team, designing and developing secure, scalable infrastructure and applications. Responsibilities include Infrastructure as Code, Red Hat Linux environments, troubleshooting, automation, remediation, SDLC practices, AI-assisted engineering adoption, cloud-native development, resiliency, and security. The role also evaluates vendor and architectural solutions, supports operational stability, and contributes to technical standards and enterprise engineering practices.

Leads software engineering within JPMorganChase Corporate Technology, designing, developing, testing, and supporting secure, scalable infrastructure and applications. Responsibilities include Infrastructure as Code, Red Hat Linux development, cloud-native engineering, automation, troubleshooting, resiliency, and SDLC practices. The role drives responsible adoption of AI-assisted engineering tools, coaches teams on secure AI usage, improves operational stability, and evaluates vendor and architectural solutions in an enterprise financial services environment.

Leads development of secure, scalable software solutions within an agile financial-services environment. Responsibilities include designing, coding, testing, debugging, reviewing production code, improving application stability through automation, applying AI-assisted engineering practices responsibly, and evaluating vendor and architectural solutions. The role requires advanced Python expertise, cloud-native experience, SDLC proficiency, software security knowledge, and familiarity with banking technology systems.

Leads full-stack software engineering teams, technical strategy, cloud-native architecture, and large-scale distributed system delivery. Responsibilities include managing engineering teams, mentoring leaders, driving technology roadmaps, overseeing Agile programs, aligning engineering execution with business goals, and ensuring reliable, high-performance solutions. The role involves collaboration with product, architecture, and platform leaders, occasional coding or code evaluation, succession planning, and adoption of modern technologies including AWS, microservices, containers, databases, and AI-based development tools.

Leads software engineering for credit trading technology, designing and building secure, scalable Python systems and AI-enabled trading platforms. Partners with sales, trading, product, vendors, and support teams to deliver solutions, improve reliability, automate remediation, and modernize systems. Establishes engineering standards for AI-assisted development, testing, security, and responsible AI use. Requires strong cloud-native AWS, CI/CD, agile, application resiliency, and software development lifecycle expertise.

Leads software engineering and SRE initiatives for secure, scalable production platforms. Designs Python or Java automation, LLM and agent solutions, data pipelines, monitoring, and resiliency practices. Owns application security, risk controls, compliance, lifecycle management, incident response, and service-level objectives. Coordinates execution across engineering teams, promotes AI-assisted development, and coaches teams on secure, responsible AI adoption.

Leads software design, development, testing, troubleshooting, and production support for secure, scalable enterprise applications. Builds and maintains code using Python or Java, AWS services, PySpark, databases, CI/CD tools, and Terraform. Develops architecture artifacts, data visualizations, and pipelines; improves system resiliency, observability, and coding practices. Resolves production issues, promotes AI-assisted engineering, and contributes to technical communities and agile team culture.

Leads software and data engineering work within an agile team, designing, developing, debugging, and supporting secure production applications and data pipelines. Builds AWS cloud-native solutions using Python, Java, SQL, and Databricks; reviews code; automates recurring remediation; improves SDLC practices; and promotes responsible AI-assisted engineering. Evaluates vendor and architectural solutions while guiding teams on secure, resilient, and compliant development practices.

Leads software engineering within an agile banking technology team. Designs, develops, secures, tests, troubleshoots, and operates scalable applications using Python, Java, Spring, and AWS. Reviews code, improves automation and operational resilience, promotes responsible AI-assisted engineering practices, and evaluates architectural solutions with vendors and internal teams. The role requires strong cloud-native development, CI/CD, web security, system design, and production support experience.

Lead software engineering for secure, reliable, scalable enterprise applications. Responsibilities include designing and delivering production code, troubleshooting complex issues, leading resiliency reviews and major incident response, improving application performance and stability, establishing service level indicators and objectives, reducing operational toil, and guiding engineers on modern technologies, cloud-native architecture, automation, observability, containers, and continuous delivery.

Designs, develops, and leads complex software solutions within PNC’s Data and Automation organization. The role aligns business strategy with software architecture, codes solutions, documents technical processes, leads problem solving, and provides technical guidance. Required expertise includes AWS services such as SageMaker, Bedrock, EMR, Lake Formation, and DataZone, along with Infrastructure as Code. The position is based in Pittsburgh or Dallas and requires in-office work.

Leads development of full-stack, cloud-native software using Java, Python, and AWS. Builds secure microservices, APIs, and LLM-powered capabilities; establishes engineering, testing, reliability, observability, and CI/CD standards; mentors engineers; drives responsible AI-assisted development; and manages production operations, incident response, and technical direction across agile initiatives.

Lead end-to-end delivery of cloud-native, full-stack software initiatives, defining architecture, engineering standards, reliability practices, observability, CI/CD, and secure production operations. Build microservices, APIs, and AI capabilities using LLMs and generative AI. Guide responsible AI-assisted development, lead incident response and continuous improvement, mentor engineers, and align technical priorities across stakeholders and initiatives.

Leads software engineering within JPMorganChase’s Risk Technology team, providing technical direction and developing secure, scalable production code. Designs, tests, debugs, and supports applications while guiding engineers, contractors, and vendors. Drives adoption and governance of AI-assisted engineering practices, improves SDLC automation, establishes validation standards, and serves as a technical subject matter expert. Requires advanced Python, Pandas, NoSQL, Databricks, cloud-native experience, architecture expertise, and strong independent problem-solving and leadership skills.

Principal Software Engineer responsible for modernizing enterprise developer tooling and improving developer productivity. Designs and codes software solutions, supports C#, Java, Python, Node.js, frameworks, plugins, and extensions, and leads adoption of development technologies. Builds reference architectures, reusable solutions, Inner Source contributions, automation, and AI-assisted development capabilities. Provides technical leadership, solves complex engineering problems, evaluates releases, reduces developer workflow friction, and guides development teams.

Leads the design and implementation of large-scale, cloud-native full-stack systems for cloud operations resilience. Develops distributed microservices using Python, Go, JavaScript, TypeScript, AWS, Docker, and Kubernetes. Defines platform roadmaps, architecture, engineering standards, and operational practices while improving reliability, performance, and technical debt. Mentors engineers, leads design reviews and cross-functional initiatives, evaluates emerging AI technologies, and influences enterprise technology strategy.

Lead development of agentic-AI capabilities and an evaluation/provenance harness to safely automate bulk code changes. Design CI/CD integrations, PR-provenance controls, observability, test/eval tooling, rollout of standards, and operational support to scale machine-authored changes while ensuring auditability, security, and reliability.

Leads development of secure, scalable software solutions for network automation. Designs, codes, tests, debugs, and reviews production systems using Python, React, Django, and FastAPI. Promotes AI-assisted engineering practices with strong validation, security, and responsible-use standards. Builds microservices, APIs, and event-driven solutions; improves application resiliency and automation. Partners with internal teams, vendors, and startups to evaluate architectures and deliver sustainable technical outcomes in an agile environment.

Leads development of secure, scalable Python backend and AWS cloud-native applications using microservices, serverless, containers, and infrastructure as code. Designs distributed systems, APIs, data stores, CI/CD pipelines, observability, resiliency, and security controls. Troubleshoots production incidents, conducts root-cause analysis, optimizes performance, modernizes dependencies, promotes AI-assisted engineering practices, and mentors team members while collaborating with product, platform, SRE, security, and data teams.

Modernize legacy SAS data-processing systems by translating logic into scalable, high-performance Python solutions. Build and optimize large-scale data pipelines using formats including SAS, Parquet, JSON, and SQL. Integrate PostgreSQL, implement parallel processing, automated testing, data validation, and quality assurance, and maintain well-documented code. Collaborate with IT specialists and stakeholders to validate requirements, troubleshoot issues, and deliver cloud-native modernization solutions.