Lead AI Engineer

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

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.

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.