About the department

This role sits within Franklin Templeton's AI Platform and AI Investment Group, which builds and runs the shared data, engineering, and AI capabilities used across investment, distribution, and corporate functions. The group's work determines how quickly and how safely the firm can put AI and advanced analytics in front of investment professionals and clients. The group operates as cross-functional agile teams working in short delivery cycles against shared engineering standards, and partners closely with investment professionals, product, data, architecture, Legal, Risk and Compliance, Global Investment Solutions, and AI governance stakeholders.

Teams are deliberately small, hands-on, and multi-disciplinary, pairing software engineers, data and AI specialists, and technical leads. The culture is evidence-driven and collaborative: team members challenge assumptions, document limitations, verify AI outputs against source data, and advance work only when the evidence and controls support the next stage.

This role exists to own the design and delivery of significant services and components within the AI Platform, and to raise the engineering standard of the teams around it.

The Senior Software Engineer will lead technical design for features and integrations, build production services, set testing and observability expectations, and mentor less experienced engineers.

Success means the platform gains capabilities that are secure, observable, and supportable, with design decisions documented and technical risk surfaced early rather than discovered in production.

How you will add value

Core Responsibilities

• You will own the technical design and delivery of backend services, APIs, data integrations, and reusable libraries within an assigned domain.
• You will translate ambiguous requirements into architecture options, estimates, sequencing, and clear acceptance criteria.
• You will set and uphold standards for automated testing, code quality, secure coding, performance, and operational readiness.
• You will build observability, structured logging, alerting, and failure handling into services so that issues are detectable before users report them.
• You will lead code and design reviews, mentor engineers, and raise the overall technical capability of the team.
• You will diagnose complex production incidents, drive root-cause analysis, and deliver systemic fixes rather than point remedies.
• You will partner with product, architecture, data, and Legal, Risk and Compliance to ensure solutions meet enterprise security and control requirements.
• You will maintain high- and low-level technical documentation and evaluate new tools and approaches against measurable delivery value.

Strategic Responsibilities

• You will approach system design, architecture, or technical decisions by considering where AI can meaningfully improve the solution, not just the development process.
• You will evaluate trade-offs between AI-assisted and traditional approaches with practical judgement.
• You will identify repetitive, manual, or error-prone processes in the team's work and introduces AI-powered improvements, including code reviews, AI-assisted testing, intelligent alerting, or document generation.
• You will apply advanced prompting techniques to tackle ambiguous, multi-step, or domain-specific problems.
• You will think through how AI-assisted solutions will be tested, deployed, monitored, and maintained.

Technical Environment

• Core stack: Python, SQL, modern backend services and APIs, with supporting data-processing and testing libraries.
• Operating environment: Cloud or hybrid environments, containerized services, secure enterprise data integrations, and approved model endpoints.
• Workflow: Git-based development, CI/CD, automated testing, agile delivery, and infrastructure or configuration as code.
• Quality & reliability: Observability, structured logging, performance testing, security controls, code-quality gates, and incident management.

What will help you be successful in this role

• Experience, Education & Certifications
• Bachelor's degree in Computer Science, Engineering, or a related discipline, or equivalent practical experience.
• 5+ years of professional software engineering experience, including production backend, distributed, or data-intensive systems.
• Demonstrated experience with automated testing, CI/CD, containerization, observability, secure coding, and operational support.
• Track record of leading technical design for non-trivial features and mentoring other engineers.
• Experience in asset management, wealth management, portfolio analytics, trading, or risk technology would be a plus.
• Hands-on experience with LLM applications, AI agents, retrieval-augmented generation, or model evaluation would be a plus.
• Experience with cloud platforms, Kubernetes, workflow orchestration, event-driven systems, or time-series data would be a plus.
• Experience moving data-science prototypes into secure, supportable production services would be a plus.

Technical Skills

• Advanced Python skills and strong experience with APIs or microservices, SQL, data processing, software design, and system integration.

Soft Skills

• Strong written and verbal communication skills, sound technical judgement, and the ability to collaborate across engineering, product, and control functions
• Advanced English proficiency

Why join Franklin Templeton

Future thinking 

• Shape the future of asset management through cutting-edge technology and global market leadership.

Welcoming culture

• Grow alongside talented colleagues in a collaborative environment that values diverse perspectives.

True partnership

• Thrive with strong benefits, competitive leave, and community support that extends beyond the workplace.