Senior Software Engineer, Network Operational Intelligence

Job Summary

Senior Software Engineer, Network Operational Intelligence(Maersk)

Job Level 4 (Senior Software Engineer)

This position will be based in Bangalore

A.P. Moller - Maersk

The Brief

A globally focused shipping and transportation organisation is continuing to drive an ambitious and complex change and transformational programme to deliver a service of excellence for its customers and clients worldwide. The Global CTIO has a mandate from the Maersk board to lead the Technology transformation of Maersk which will enable the Maersk business strategy to become the global integrator of container logistics through digitising the Maersk business processes and customer engagement.

Scope

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.

Specific Job Requirements

We are looking for a Senior Software Engineer (8+ years) with a product mindset - a strong software engineering foundation combined with hands-on applied AI/LLM engineering experience. The role holder will demonstrate deep expertise in Python-based platform development and in building LLM-powered systems for production use.

Software Engineering Expertise

• 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.

AI Agentic Engineering Expertise

• 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.