We're hiring a Full Stack Software Engineer to build the infrastructure that powers our AI agents and ML systems end-to-end — from UX/UI, fine-tuning foundation models to shipping production-grade agent harnesses. You'll work across the stack: Creating UX design and UI in ReactJS/TS, building MLOps pipelines, customizing LLMs, and deploying scalable agent systems on Kubernetes. This role sits at the intersection of UX design, ML engineering, platform engineering, and applied AI.
Responsibilities- Design UX and build UI for Agentic Ops
- Design and build agent harnesses in Python — the runtime scaffolding that enables AI agents to perceive, reason, plan, and act reliably
- Develop and maintain a robust MLOps framework using Kubeflow and complementary tooling (MLflow, Argo, Airflow, or similar) to orchestrate training, evaluation, and deployment workflows
- Fine-tune foundation LLMs using techniques such as LoRA/QLoRA, SFT, and RLHF; manage datasets, training runs, and evaluation pipelines
- Deploy and operate services on Kubernetes, including model serving, autoscaling, and observability
- Build and integrate AI agents using modern agent frameworks (LangGraph, CrewAI, AutoGen, LlamaIndex, or similar)
Apply software engineering rigor — SOLID principles, secure coding, static analysis, code reviews, and CI/CD — across all deliverables
- Bachelor’s or Master’s degree in Engineering, along with around 8+ years of experience in Python development, including building and supporting production systems
- Hands-on experience working with agent-based or agentic systems, using at least one framework such as LangGraph, CrewAI, AutoGen, LangChain, or LlamaIndex
- Exposure to designing or contributing to MLOps pipelines, with familiarity with tools like Kubeflow
- Practical experience in fine-tuning large language models (for example, open-source models like Llama, Mistral, Qwen, or similar)
- Experience deploying containerized applications on Kubernetes, including areas like Helm, operators, networking, and resource management
- Familiarity with at least one major cloud platform (AWS, GCP, or Azure), including services related to compute, storage, identity access management, and machine learning
- Understanding of software engineering practices such as modular design (SOLID principles), design patterns, secure coding practices, static analysis tools (for example, mypy, ruff, Bandit, SonarQube), and testing approaches (unit and integration testing)
Nice to Have:
- Exposure to distributed training approaches, using tools such as DeepSpeed, FSDP, or Accelerate
- Familiarity with vector databases, retrieval-augmented generation (RAG) systems, and evaluation frameworks for language models
- Experience working with model serving solutions such as vLLM, TGI, KServe, or Triton
Skills Required
- Strong ReactJS/TypeScript skills
- Strong Python engineering skills with a track record of building production systems
- Hands-on experience building agent harnesses or agentic systems using at least one framework (LangGraph, CrewAI, AutoGen, LangChain, LlamaIndex, etc.)
- Experience designing or contributing to MLOps pipelines, with working knowledge of Kubeflow
- Practical experience fine-tuning foundation LLMs (open-source models such as Llama, Mistral, Qwen, or similar)
- Proficiency deploying containerized workloads on Kubernetes (Helm, operators, networking, resource management)
- Working knowledge of at least one major cloud provider (AWS, GCP, or Azure) — compute, storage, IAM, and managed ML services
- Solid grasp of software engineering best practices: SOLID, design patterns, secure coding (OWASP), static analysis (e.g., mypy, ruff, Bandit, SonarQube), unit/integration testing
- Bachelor's degree in Computer Science, Engineering, or equivalent practical experience
- Experience with UX design using Figma
- Experience with distributed training frameworks (DeepSpeed, FSDP, Accelerate)
- Familiarity with vector databases, RAG architectures, and evaluation frameworks for LLMs
- Experience with model serving frameworks (vLLM, TGI, KServe, Triton)
Nokia Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Nokia and has not been reviewed or approved by Nokia.
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Equity Value & Accessibility — Equity programs include a global employee share purchase plan with company matching and multi‑year share awards. These mechanisms broaden participation and tie rewards to long‑term outcomes.
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Healthcare Strength — Health coverage includes major medical plans with supplementary options such as vision, legal services, and care navigation. The range of offerings indicates comprehensive support for medical needs.
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Parental & Family Support — A global policy grants paid leave for new parents regardless of gender and provides structured return‑to‑work support. Company‑paid life insurance further strengthens family protection across regions.
Nokia Insights
What We Do
At Nokia, we create technology that helps the world act together. As a trusted partner for critical networks, we are committed to innovation and technology leadership across mobile, fixed and cloud networks. We create value with intellectual property and long-term research, led by the award-winning Nokia Bell Labs. Adhering to the highest standards of integrity and security, we help build the capabilities needed for a more productive, sustainable and inclusive world.
