Advanced Software Engineering Lead

Infosys Singapore & Australia; Singapore; On-site; SGD 180,000 - 260,000; Full time

Job summary

Infosys Singapore & Australia seeks a senior, hands-on technologist within Advanced Software Engineering. You will be a polyglot across software engineering and cloud, with practical hands-on AI experience, capable of leading multiple client engagements and translating complex tech into business value.

The role emphasizes architecture, cloud migrations, and DevOps, requiring adeptness across Python/Go/Java/TypeScript, and modern distributed architectures.

Qualifications

• Proven hands-on polyglot software engineering across multiple stacks.
• Deep cloud experience with Azure, AWS, and/or GCP and migration planning.
• Strong DevOps focus with infrastructure as code and CI/CD automation.

Responsibilities

• Architect and implement high-performance solutions across diverse stacks.
• Lead technical delivery, architectural reviews, and migrations.
• Design, integrate, and operate AI-enabled platforms and accelerators.
• Manage multiple client engagements with rapid domain learning.
• Advise executives on technical strategy and business outcomes.

Skills

Polyglot software engineering

Multi-cloud & migration

Modern DevOps & IaC

Tools

Terraform

Bicep

Job description

Primary Objective: Act as a senior, hands‑on technologist within the Advanced Software Engineering Practice. The ideal candidate is a true polyglot across software engineering and cloud domains, with practical hands‑on experience in Al engineering. This role demands an adaptable leader capable of context‑switching across multiple complex client engagements-from analysing hybrid systems to building modern Al‑infused platforms-while seamlessly navigating rapidly changing technology and business contexts.

Key Responsibilities:

• Polyglot Execution: Architect and implement high‑performance solutions across varied technology stacks (e.g., Python, Go, Java, TypeScript) and application architectural paradigms.
• Cloud‑Agnostic Leadership: Lead technical delivery, architectural reviews, and migration planning across any major cloud (Azure, AWS, GCP) and hybrid environments.
• Hands‑on Al Engineering: Design, integrate, and operationalize complex technology solutions, including orchestration & accelerators.
• Multi‑Engagement Management: Handle multiple, simultaneous client portfolios. Rapidly digest new business domains, assess technical debt, and pivot strategies based on changing client needs.
• Strategic Consulting: Act as a trusted technical advisor to client executives and engineering teams, translating deeply technical concepts into clear business outcomes.

Required Technical Skills:

• Polyglot Software Engineering: Deep, hands‑on expertise in multiple programming languages and modern distributed architectures (Microservices, Event‑Driven, Serverless).
• Multi‑Cloud & Migration Proficiency: Advanced capabilities across Azure, AWS, and/or GCP. Proven experience auditing existing infrastructures, capacity profiling, and designing target state architectures for complex cloud migrations.
• Modern DevOps & laaC: Mastery of Infrastructure as Code (e.g., Terraform, Bicep) and robust CI/CD pipeline automation.

Soft Skills & Consulting Experience:

• Extreme Adaptability: Thrives in ambiguous, high‑pressure environments where technology stacks and business requirements evolve constantly.
• Cognitive Flexibility: Exceptional ability to context‑switch between disparate projects, client cultures, and technical constraints without losing momentum.
• Executive Communication: Highly effective at presenting complex architectural findings and migration roadmaps to both technical stakeholders and C‑level executives.
• Local Market Presence: Melbourne‑based, with the ability to foster strong relationships within the local tech ecosystem and client base.

Nice to have [At least 12 months experience]:

• Hands‑on Al Engineering: Design, integrate, and operationalize applied Al solutions, including LLM orchestration, RAG architectures, and Al‑driven accelerators.
• Applied AI / ML: Practical experience building with foundational models, agentic frameworks, prompt engineering, and deploying models to production (MLOps/LLMOps).