Advanced Software Engineering Lead

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

Job summary

Infosys Consulting is seeking a senior technologist to drive advanced software engineering and AI initiatives in a cloud-agnostic context. You will apply polyglot coding skills across Python, Go, Java, and TypeScript to architect scalable solutions and lead hybrid/multi-cloud migrations.

In this role you will guide technical delivery, perform architectural reviews, and design AI-enabled platforms while managing multiple client engagements.

Qualifications

• Extensive hands-on experience across Python, Go, Java, TypeScript.
• Proven ability to lead cloud migrations across Azure, AWS and GCP.
• Strong DevOps and IaC experience with Terraform, Bicep, CI/CD.
• Experience advising clients and translating tech into business value.

Responsibilities

• Architect and implement high-performance solutions across varied stacks.
• Lead technical delivery, architectural reviews, and migrations.
• Design, integrate, and operationalize AI-enabled platforms.
• Manage multiple client engagements and adapt strategies.
• Translate technical findings into clear business outcomes for executives.

Skills

Polyglot software engineering

Multi-cloud & migration

DevOps & IaC

Tools

Terraform

Bicep

CI/CD pipelines

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 AI engineering. This role demands an adaptable leader capable of context-switching across multiple complex client engagements—from analysing hybrid systems to building modern AI-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 AI 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 & IaaC: 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 AI Engineering: Design, integrate, and operationalize applied AI solutions, including LLM orchestration, RAG architectures, and AI-driven accelerators.
• Applied AI / ML: Practical experience building with foundational models, agentic frameworks, prompt engineering, and deploying models to production (MLOps/LLMOps).