Senior Software Engineer, Network Operational Intelligence

The Network Ops Intelligence team at Maersk is responsible for enabling, automating, and enhancing the management of data networks across Core, Edge, and Cloud locations (offices, warehouses, terminals, vessels, data centres, cloud environments, etc.), and is expanding into AI-driven network operations — building intelligent, autonomous systems for observability, assurance, and decision-making.

This role sits at the intersection of deep software engineering and applied AI : designing and building production-grade platforms and tools, and infusing them with Generative AI, Large Language Models (LLMs), and agentic frameworks to transform how networks are operated.

Key areas include but are not limited to:

Automation & Orchestration: Design and implementation of workflows and systems to automate provisioning, configuration, monitoring, and assurance of network services.

AI-Augmented Operations: Develop agentic AI solutions using LLMs, orchestration frameworks and vector stores to enable intelligent automation, natural language interfaces, and autonomous troubleshooting.

Tooling & Platform Development: Build scalable internal tools for network inventory, monitoring, telemetry collection, event correlation, and intent-based management.

Intelligent Observability & Insights: Use GenAI to augment monitoring and assurance — alert summarization, root cause inference, change impact prediction, and knowledge extraction from telemetry, syslogs, traps, and alarms.

Prompt Engineering & Reasoning: Design structured/dynamic prompts, chains, and Retrieval-Augmented Generation (RAG) workflows for context-aware insights from large-scale network data.

Our mission is to drive network intelligence, automation, and operational excellence by bridging infrastructure with software and AI — building LLM-based platforms that are autonomous, explainable, and user-friendly.

Advanced Python proficiency: Mastery of Python syntax, data structures, OOP principles, and AI/NLP libraries (LangChain, LangGraph, Transformers, OpenAI SDK, HuggingFace, LlamaIndex).

Web frameworks: Hands-on expertise with FastAPI, Flask, or Django for backend service development; ability to lead projects and mentor developers.

API development leadership: Design and development of secure, high-performance RESTful APIs, with strong understanding of authentication mechanisms and integration patterns.

Databases & ORM: In-depth experience with relational databases (PostgreSQL, MySQL, SQLite) and ORM frameworks (SQLAlchemy, Django ORM).

Microservices & Docker: Architecting modular, scalable, resilient microservices-based systems with containerization.

CI/CD & DevOps: GitHub workflows, Jenkins, automated build/test/deploy pipelines.

Testing & quality: TDD discipline with PyTest/unittest; strong focus on code quality, maintainability, and reproducibility.

Algorithms & problem-solving: Design, analysis, and optimization of algorithms; strategic problem-solving for complex challenges.

Hands-on LLM experience: Building applications with foundation models (OpenAI, Claude, LLaMA, Mistral, Gemini) via APIs and self-hosted/open-weight models.

Agentic AI systems: Designing autonomous/semi-autonomous agents with multi-step reasoning, tool/function calling, planning, memory, and state management — using LangGraph, LangChain, or similar frameworks (e.g., AutoGen, CrewAI, Semantic Kernel, Model Context Protocol (MCP) for tool integration).

Prompt engineering & RAG: Structured prompts, few-shot and chain-of-thought techniques, embedding models, vector stores (FAISS, Chroma, Weaviate, pgvector), hybrid search, chunking strategies, and re-ranking for retrieval pipelines.

LLMOps & evaluation: Systematic evaluation of LLM outputs — golden datasets, LLM-as-judge, regression testing of prompts/chains; observability and tracing of agent workflows (e.g., LangSmith, Langfuse, OpenTelemetry for GenAI); handling hallucination mitigation, fallback mechanisms, and graceful degradation.

Fine-tuning & model adaptation : Familiarity with parameter-efficient fine-tuning (LoRA/PEFT), instruction tuning, and when to choose fine-tuning vs. RAG vs. prompting.

Structured outputs & tool integration : Function calling, JSON-schema-constrained generation, and integrating LLMs securely with APIs, databases, and monitoring platforms.

MLOps & inference infrastructure: Containerized AI workloads, GPU orchestration, batch vs. streaming inference, model serving (e.g., vLLM, Triton), token/cost optimization, caching, and rate limiting.

AI security & governance : Data privacy, PII redaction, prompt-injection defenses, guardrails frameworks, role-based access, auditability, and responsible/ethical deployment of LLM systems at enterprise scale.

Multi-modal & emerging capabilities : Experience with multi-modal models (text + logs/metrics/diagrams), streaming responses, and voice/chat interface design.

Collaborate closely with Product Owners, Network SMEs, and Engineers to gather requirements and identify AI opportunities in telemetry analysis, event correlation, configuration analysis, and operational summarization.

Own end-to-end development of network tooling services across telemetry, topology, configuration management, and orchestration — and augment them with GenAI capabilities.

Architect and deploy agent-based systems that integrate with network management platforms and decision-support systems.

Design and implement prompt flows, vector-based search, and retrieval pipelines to contextualize large-scale telemetry and logs.

Build natural-language interfaces enabling intelligent self-service for operations teams (querying, reasoning, diagnostics, root cause analysis).

Design, implement, and publish high-performance APIs serving Maersk's network operations ecosystem.

Drive architectural decisions; mentor engineers on software engineering best practices and on LLM usage, architecture, evaluation, and security in production.

Support program delivery with estimation, planning, and risk assessment; participate in capacity planning, performance optimization, and reliability initiatives.

Continuously evaluate emerging GenAI models, agent frameworks, and tools for Ops Intelligence use cases.

Experience in network operations, network automation, observability, or infrastructure monitoring.

Experience with time-series/telemetry data platforms (Kafka, Prometheus, Elasticsearch/OpenSearch).

Exposure to Kubernetes for orchestrating microservices and AI workloads

To be considered for this opportunity candidates will have the following skills:

Demonstrates excellent communication skills with ability to communicate effectively at all levels within the organisation reaching out when needed to resolve issues.

Shows strength in negotiation both internally and externally to ensure successful resolution of issues prior to escalation, ensuring that the wider business imperative is not undermined.

A clear understanding of the business imperatives and how they align with the wider company strategy.

Maersk is committed to a diverse and inclusive workplace, and we embrace different styles of thinking. Maersk is an equal opportunities employer and welcomes applicants without regard to race, colour, gender, sex, age, religion, creed, national origin, ancestry, citizenship, marital status, sexual orientation, physical or mental disability, medical condition, pregnancy or parental leave, veteran status, gender identity, genetic information, or any other characteristic protected by applicable law. We will consider qualified applicants with criminal histories in a manner consistent with all legal requirements.

We are happy to support your need for any adjustments during the application and hiring process. If you need special assistance or an accommodation to use our website, apply for a position, or to perform a job, please contact us by emailing accommodationrequests@maersk.com .