Lead AI Engineer

Company Description

IFS is a billion-dollar revenue company with 7000+ employees on all continents. Our leading AI technology is the backbone of our award-winning enterprise software solutions, enabling our customers to be their best when it really matters–at the Moment of Service™. Our commitment to internal AI adoption has allowed us to stay at the forefront of technological advancements, ensuring our colleagues can unlock their creativity and productivity, and our solutions are always cutting-edge.

At IFS, we’re flexible, we’re innovative, and we’re focused not only on how we can engage with our customers but on how we can make a real change and have a worldwide impact. We help solve some of society’s greatest challenges, fostering a better future through our agility, collaboration, and trust.

We celebrate diversity and understand our responsibility to reflect the diverse world we work in. We are committed to promoting an inclusive workforce that fully represents the many different cultures, backgrounds, and viewpoints of our customers, our partners, and our communities. As a truly international company serving people from around the globe, we realize that our success is tantamount to the respect we have for those different points of view.

By joining our team, you will have the opportunity to be part of a global, diverse environment; you will be joining a winning team with a commitment to sustainability; and a company where we get things done so that you can make a positive impact on the world.

We’re looking for innovative and original thinkers to work in an environment where you can #MakeYourMoment so that we can help others make theirs. With the power of our AI-driven solutions, we empower our team to change the status quo and make a real difference.

If you want to change the status quo, we’ll help you make your moment. Join Team Purple. Join IFS.

 

Job Description

As a Lead AI Engineer, you will design and build applied AI solutions that drive measurable business value from concept through scalable production deployment. You'll architect enterprise AI systems leveraging large language models, retrieval-augmented generation, and agentic workflows while leading technical strategy and mentoring engineering teams.

Key Responsibilities

• Design and architect AI-powered systems using LLMs, RAG, agentic workflows, and orchestration patterns integrated with enterprise data and business processes
• Develop secure, maintainable, production-ready software platforms and cloud-native services that orchestrate models, tools, retrieval systems, and enterprise workflows
• Build rapid prototypes and proof-of-concepts to validate new technologies and identify business opportunities
• Establish comprehensive evaluation, monitoring, and quality practices including testing, benchmarking, observability, and continuous improvement
• Lead technical design discussions, architecture reviews, and drive engineering best practices across teams
• Mentor engineers and develop reusable AI capabilities and frameworks that accelerate delivery across the organization
• Collaborate with product teams, architects, domain experts, customers, and partners to identify opportunities and deliver business impact
• Influence IFS's AI strategy and long-term technology direction through hands-on delivery, experimentation, and customer engagement, including external-facing innovation through industry events and partner collaboration

Qualifications

Core Requirements 

• Bachelor’s degree in computer science, Software Engineering, AI, Data Science, or a related field. Master's degree is advantageous. 

• 8+ years of professional experience in AI, Machine Learning, and/or Software Engineering, backed by a proven track record of successfully delivered projects. 

• Experience bringing incubated AI solutions to production, including scoping, design, development, testing, deployment, and vigilant monitoring. 

• Strong programming skills one or more mainstream programming languages such as Python, Golang, C# or TypeScript. 

• Experience with context engineering, including retrieval architecture, embeddings, vector databases, search technologies, and retrieval optimization techniques. 

• Strong backend engineering fundamentals, including APIs, distributed services, cloud-native architectures, CI/CD, integration, automation, and security. 

• A solid background in DevOps and MLOps/LLMOps practices, and familiarity with tools to manage infrastructure as code, like Terraform and package managers like Helm Charts. 

• Ability to design solutions that integrate enterprise applications, business processes, workflows, and data platforms. 

Applied AI & Architecture 

• Experience designing and implementing AI-driven architectures using LLMs, retrieval-augmented generation (RAG), agentic workflows, orchestration patterns, and enterprise data sources. 

• Strong understanding of the AI system lifecycle, including evaluation, deployment, monitoring, governance, and continuous improvement. 

• Experience with MLOps lifecycles, deployment pipelines, model operations, and observability for AI systems is advantageous. 

Collaboration & Execution 

• Experience working closely with customers, stakeholders, and domain experts to define and deliver solutions. 

• Demonstrated ability to rapidly prototype, experiment, measure outcomes, and iterate quickly in real-world customer and enterprise environments. 

• Strong communication skills, with the ability to explain complex technical concepts clearly to technical and non-technical audiences. 

• Comfortable operating in ambiguous, fast-moving environments, translating complex business problems into clear technical strategies, execution plans, and measurable outcomes. 

• Experience leading technical discussions, influencing architectural direction, mentoring engineers, and driving alignment across teams. 

• Track record of influencing technical direction and technology strategy through hands-on delivery, experimentation, and evidence-based recommendations. 

Experience with two or more of the following technologies is highly desirable 

• LLM Serving & AI Platforms 

• vLLM, LiteLLM, KServe or similar LLM serving platforms. 

• AI gateways, model routing, inference serving and multi-modal orchestration. 

• Foundation Model 

• Cohere, OpenAI, Anthrophic, Llama, Mistral, DeepSeek or other open-source LLMs. 

• Agentic AI 

• LangGraph, PydanticAI, Semantic Kernel, CrewAI, AutoGen or similar agentic AI frameworks. 

• Tool calling, MCP, workflow orchestration and autonomous agents. 

• AI Evaluation & Observability 

• MLFlow, DeepEval, Ragas, Promptfoo, Langfuse or similar evaluation and observability tools. 

• Cloud & Infrastructure 

• Kubernetes, Docker, Helm, Terraform and cloud-native deployments platforms. 

• GPU infrastructure and inference optimization. 

• Model Development 

• Hugging Face ecosystem (Transformers, PEFT, LoRA). 

• Fine-tuning, model evaluation, benchmarking, prompt engineering and model optimization. 

• Experience building enterprise AI platforms or developer tooling. 

• Experience working with AMD, NVIDIA, or other AI accelerator technologies. 

• Experience with enterprise software domains such as ERP, EAM, Service Management, Manufacturing, Supply Chain, or Field Service. 

• Experience building customer-facing demonstrations, proof-of-concepts, or innovation showcases. 

• Contributions to open-source AI projects, technical communities, conferences, or publications. 

• Experience working with Microsoft Azure, AWS, or Google Cloud AI services. 

Nice to Have

 

• Experience building enterprise AI platforms or developer tooling. 

• Experience working with AMD, NVIDIA, or other AI accelerator technologies. 

• Experience with enterprise software domains such as ERP, EAM, Service Management, Manufacturing, Supply Chain, or Field Service. 

• Experience building customer-facing demonstrations, proof-of-concepts, or innovation showcases. 

• Contributions to open-source AI projects, technical communities, conferences, or publications. 

• Experience working with Microsoft Azure, AWS, or Google Cloud AI services.

Additional Information

We embrace flexibility and hybrid work opportunities to support diverse needs and lifestyles, while also valuing inclusive workplace experiences. By fostering a sense of community, we drive innovation, strengthen connections, and nurture belonging. Our commitment ensures you can work in a way that suits you best, while also engaging with colleagues to share ideas and build meaningful relationships.