Strength. Care. Growth
You will know we are the right place for you, if you are driven by:
- Opportunities to learn and build your career.
- Meaningful work in a stable and fast-paced company.
- Diversity of people, projects, and platforms.
- A supportive, fun, and inspiring place to work.
This job can be performed by all countries within our A1 footprint.
Role Overview:
You will design the end?to?end architecture of our AIaaS platform and lead the forward?deployed engineering work that brings real AI use cases into production. In this hybrid role, you define how core AI components integrate into the stack while collaborating closely with business and product teams to prototype and deploy production?grade solutions.
RoleInsights:
- AI Architecture & Platform Design defines the target architecture for the AIaaS application layer and integrates key components such as model serving, vector search, RAG, prompt management, fine?tuning and agent orchestration.
- Forward Engineering & Implementation delivers high?impact AI use cases by collaborating with business teams to scope, design, build and deploy solutions on the platform.
- LLMOps & MLOps Integration accelerates application delivery by establishing standardized patterns, SDKs and templates for pipelines, evaluation, guardrails, context retrieval and telemetry.
- Vendor & Open?Source Evaluation ensures optimal performance and scalability by benchmarking foundation models, embeddings, vector databases and AI frameworks.
- GPU & Inference Optimization improves inference efficiency by partnering with Cloud Platform Engineers to optimize latency, throughput, token streaming and GPU utilization.
- Enterprise Security & Governance strengthens platform integrity through architectural oversight of AI safety, privacy, RBAC, identity propagation and sovereign?cloud compliance.
- Technical Enablement & Strategy turns complex business needs into scalable designs, produces architectural blueprints and supports engineering teams adopting the AIaaS platform.
What Makes You Unique:
- End?to?end AI/ML system expertise demonstrates your ability to design, build and scale modern Generative AI architectures in production environments.
- Hands?on proficiency with Python and AI frameworks enables you to work confidently with tools such as PyTorch, Hugging Face, LangChain, LlamaIndex, Semantic Kernel and AutoGen.
- Practical RAG and vector search experience allows you to implement RAG pipelines, vector indexing, hybrid search and effective prompt engineering strategies.
- Model deployment and optimization skills support your work with open?source and proprietary models, quantization techniques and specialized inference servers like vLLM and Triton.
- Cloud?native and API fluency ensures you understand how AI application layers interact with Kubernetes, microservices and REST/gRPC APIs.
- Client?facing engineering mindset helps you translate ambiguous business needs into production?ready software architecture.
- Cross?functional collaboration strength enables you to work effectively with infrastructure, SRE, security and product teams to drive production readiness.
Nice to Have:
- Forward?Deployed Engineering or Solution Architecture experience adds value when working in enterprise AI environments.
- Model fine?tuning knowledge supports advanced workflows using LoRA, QLoRA, PEFT and synthetic data generation.
- Familiarity with AI guardrail frameworks strengthens platform safety through tools like NeMo Guardrails, Guardrails AI and evaluation suites such as Ragas, TruLens and DeepEval.
- Understanding of sovereign?cloud and telecom governance helps you operate within strict regulatory and compliance environments.
- Experience with orchestration and pipeline tools enhances your ability to work with Airflow, MLflow, Ray, Kubeflow and batch processing pipelines.
Job code:AEM060P311
Job classification:P3 -11, KV5, PT 2/3
