IT Software Engineer 4 Lead Software Engineer Generative AI & Platform Engineering
About The Position
The Lead Software Engineer – Generative AI & Platform Engineering is a hands-on technical leadership role in the Emerging Technology group. The engineer leads the design, development, prototyping, and deployment of scalable software solutions for complex business challenges, translating technology concepts into secure, reliable, maintainable, production-ready enterprise solutions. The role combines software development expertise, modern architecture, cloud-native engineering, and practical experience with emerging technologies, including Generative AI, Agentic AI, Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG). The role includes leading end-to-end software engineering initiatives, providing technical direction and mentorship, establishing reusable architecture patterns and engineering standards, and collaborating with product, architecture, data science, cybersecurity, infrastructure, UX, and business teams. It also drives emerging technology initiatives from experimentation and proof of concept into enterprise production environments.
Requirements
- Bachelor's degree in Computer Science, Software Engineering, Computer Engineering, Information Technology, or a related technical discipline, with at least 8 years of relevant professional experience; candidates without a bachelor's degree must have at least 10 years of relevant professional experience.
- Strong hands-on software engineering experience developing complex applications, distributed systems, APIs, platforms, or cloud-native solutions.
- Demonstrated experience leading technical initiatives across the complete Software Development Lifecycle (SDLC).
- Strong programming proficiency in one or more modern programming languages, such as Python, Java, JavaScript/TypeScript, C#, or Go, or comparable languages.
- Strong understanding of software architecture, design patterns, system integration, API development, and distributed systems.
- Experience designing scalable, secure, observable, maintainable, and high-performing software solutions.
- Knowledge of automated testing, CI/CD pipelines, DevOps practices, and software quality engineering.
- Working knowledge of cloud platforms, containerization, infrastructure automation, and production support.
- Strong analytical, problem-solving, decision-making, and critical-thinking capabilities.
- Excellent communication skills and the ability to explain complex technical concepts to engineering and non-technical stakeholders.
Nice To Haves
- Practical experience developing or integrating AI/ML solutions, Generative AI applications, LLMs, Agentic AI, or RAG.
- Experience with vector databases, embeddings, prompt engineering, AI agents, and LLM integration frameworks.
- Demonstrated ability to move emerging technology solutions from proof of concept through pilot, production deployment, governance review, and operational support.
- Familiarity with responsible AI practices, model evaluation, AI guardrails, data privacy, cybersecurity, compliance, and enterprise AI governance.
- Experience with cloud-native and serverless architectures, Kubernetes, microservices, and event-driven architecture.
- Knowledge of API management, enterprise integration, observability, monitoring, and Infrastructure as Code (IaC).
- Experience collaborating with cross-functional teams across product, design, data, security, architecture, and business functions.
- Proven ability to mentor engineers, influence technical decisions, and establish reusable engineering standards.
- Advanced degree or relevant certifications in cloud computing, AI/ML, or software architecture.
- Contributions to open-source projects, technical communities, patents, publications, or enterprise innovation initiatives.
Responsibilities
- Lead the architecture, design, development, testing, and deployment of modern software applications, prototypes, and reusable platform capabilities.
- Evaluate, prototype, and implement emerging technologies, including Generative AI, Agentic AI, RAG, cloud-native services, APIs, and automation frameworks.
- Translate business requirements and product objectives into scalable architectures, technical roadmaps, and implementation strategies.
- Drive end-to-end Software Development Lifecycle (SDLC) activities, including requirements analysis, design, development, testing, deployment, and production support.
- Provide hands-on technical leadership, mentor engineers, conduct architecture and code reviews, and establish engineering best practices.
- Collaborate with product managers, architects, data scientists, cybersecurity specialists, infrastructure teams, UX professionals, and business stakeholders.
- Define proof-of-concept objectives, success criteria, performance benchmarks, automated testing strategies, and production-readiness standards.
- Identify technical risks, dependencies, integration challenges, security requirements, and operational considerations.
- Develop reusable architecture patterns, engineering frameworks, technical accelerators, and platform standards.
- Drive the transition of emerging technology initiatives from experimentation and proof of concept into enterprise-scale production environments.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
