Autonomous AI Solutions Engineer

At the forefront of AI innovation, we're seeking a skilled and forward-thinking engineer to join our team. As a partner to businesses and governments, we offer expertise and cutting-edge solutions that protect and enhance products, secure identities, safeguard revenues, and enforce compliance.

Our customers trust us for their expertise in R&D, security design, engineering, and data-driven insights. We're part of a c$2 billion business with over 5,000 associates worldwide.

We provide a range of software platforms with sophisticated anti-counterfeit, anti-piracy, anti-fraud capabilities and track and trace solutions capable of handling billions of transactions per day. Our customers are global brands and Governments.

In this role, you'll design, develop, and deploy modern AI solutions using Generative AI, Agentic AI frameworks, Retrieval-Augmented Generation (RAG), and Model Context Protocol (MCP) for intelligent multi-agent orchestration.

You'll build context-aware AI agents capable of dynamic memory management, tool-based reasoning, multi-modal interaction, and consistent state maintenance across distributed AI ecosystems. You'll work at the forefront of autonomous AI system design integrating multi-model workflows, RAG pipelines, tool-augmented agents, and persistent context protocols.

Key Responsibilities:

• Design, develop, and deploy advanced AI systems leveraging LLMs, multi-modal models, and autonomous agent frameworks.
• Architect and implement Retrieval-Augmented Generation (RAG) pipelines integrated with vector databases for dynamic, enterprise-safe context retrieval.
• Develop and manage context-aware AI agents leveraging Model Context Protocol (MCP) or equivalent structured context management mechanisms for persistent memory, multi-agent collaboration, and session continuity.
• Implement context propagation, state serialization, and contextual handoff mechanisms across distributed agents, tools, and reasoning chains.
• Integrate AI systems with cloud APIs, vector databases, plugin architectures, and orchestration frameworks for multi-step task execution.
• Optimize memory management, prompt engineering, and dynamic context windows for complex, long-running AI workflows.
• Lead AI agent orchestration initiatives using frameworks like LangChain, Semantic Kernel, CrewAI, or AutoGen, with MCP integration for context tracing and governance.

Required Skills and Qualifications:

• 3+ years of AI engineering experience, with direct expertise in:
• Generative AI (GenAI) frameworks (OpenAI, Hugging Face, Anthropic, Mistral)
• Agentic AI orchestration frameworks (LangChain, Semantic Kernel, AutoGen, CrewAI)
• Retrieval-Augmented Generation (RAG) system design and deployment
• Model Context Protocol (MCP) or structured context management solutions in multi-agent, multi-tool AI workflows
• Strong software engineering skills in Python, including AI/ML libraries like PyTorch, TensorFlow, Transformers, and RAG toolkits.
• Hands-on experience working with vector databases (Pinecone, FAISS, Weaviate, Chroma) and plugin-based AI architectures.
• Strong understanding of AI memory management, session context propagation, and multi-agent communication protocols.
• Programming framework/language: Python, JS or Java.
• Vector databases (Pinecone, FAISS, Weaviate, Chroma) and plugin-based AI architectures.
• Experience with ML frameworks like TensorFlow, PyTorch, or Scikit-learn.
• Solid understanding of statistics, data structures, and algorithms.

Preferred Skills:

• Experience integrating multi-modal models (text, image, audio, video) into agentic workflows.
• Familiarity with AI safety, observability, and context integrity management in distributed AI systems.
• Exposure to LLMOps pipelines, prompt optimization techniques, and AI agent performance tuning.
• Experience contributing to open-source AI orchestration frameworks or context management libraries.
• Understanding of AI alignment, ethical AI principles, and responsible AI design.
• Experience with Deep learning in NLP, computer vision, or reinforcement learning.

Education Qualification:

• Bachelor's or Master's degree in Computer Science, Machine Learning, or a related field.
• 4+ years of experience working as a software engineer developing commercial software.
• Experience using collaborative tools such as: Slack, Microsoft Teams, and Jira.

Benefits:

• Work for a market leading company.
• Nice offices with great facilities.
• Health Insurance.
• 24 days holidays plus national holidays.
• Life Insurance.