Modernization Engineer

Description

The company runs and modernizes mission-critical technology systems for large global businesses, using AI-powered insight to support smarter decisions and faster innovation.

Engineers work directly with customers to design, deploy and operate generative and agentic AI solutions. Engineering, consulting and product teams work together, and the role is judged on business results, not only on code.

The environment is flexible and supportive, with attention to well-being.

Responsibilities

• Build autonomous agents using LLMs, planning algorithms and decision frameworks on the company's agentic platform.
• Adapt the pre-built agents on that platform to each customer, aiming for wide adoption and consistent behaviour.
• Design agent architectures that handle autonomy, interaction and task completion.
• Connect agents to applications, APIs, workflows, databases, cloud services and external systems.
• Improve agent behaviour through feedback loops, reinforcement learning, semantic knowledge layers and user input.
• Monitor performance and put safeguards in place for security, reliability and responsible use.
• Work with researchers, engineers, product teams and other cross-functional partners.
• Document agent logic, design choices, dependencies and deployment practices.
• Build and maintain the internal registry for agents and tools, covering metadata and lifecycle management.
• Build the gateway for agent communication with security, rate limiting, observability and cost controls.
• Apply orchestration patterns for agents and workflows using tools such as Temporal and Airflow.
• Secure agents with authentication, authorization and data protection controls.
• Tune cost, scalability, performance and reliability across cloud and on-premises environments.
• Use knowledge graphs to improve agent reasoning and data integration.
• Deploy and customize agentic AI platforms, LLM agents and orchestration frameworks.
• Resolve technical blockers in data ingestion, model deployment and agent behaviour.
• Write and refine prompts for clarity, compliance and contextual accuracy.
• Turn business logic into agentic workflows and task trees that match real-world expectations.
• Add observability, performance metrics and feedback loops to raise reliability, latency, trust and outcomes.
• Build and iterate customer-specific AI solutions with agentic AI frameworks.
• Own delivery from scoping to production, working inside customer teams so solutions drive adoption and measurable results.
• Turn customer problems into well-fitted solutions, favouring the right outcome over the quickest one.
• Diagnose and improve system performance, scalability and reliability.
• Record deployment lessons, document best practices and share insights that improve the company's platforms and frameworks.
• Keep up with emerging technologies and shifting customer needs.

Requirements

• Bachelor's degree in computer science, engineering or an equivalent field.
• Full-stack experience with Python and front-end tools such as TypeScript and React.js, including building demos and systems from scratch.
• At least 3 years of hands-on experience building AI solutions with frameworks such as LangChain, Microsoft Semantic Kernel, Google ADK or Microsoft Agent Framework.
• Working knowledge of LLMs, agent architectures and telemetry or observability tools such as LangSmith, Langfuse and LiteLLM.
• Experience with Docker, Kubernetes and at least one cloud platform (Azure, AWS or GCP), or on-premises environments.
• Experience with microservices architectures.
• Solid grasp of the software delivery lifecycle, Git, GitHub, and data engineering tools such as Pandas and Spark.
• Experience with cloud AI platforms and distributed computing architectures.
• Able to turn business needs into technical solutions and explain their value to diverse stakeholders, including executives.
• Hands-on work with SQL databases such as PostgreSQL, NoSQL databases such as MongoDB, and vector databases for storage, semantic search and retrieval.
• Proficient in CI/CD tools such as GitHub Actions, automated testing and observability.
• Proficient in API development, back-end services, and AWS, Azure or GCP.
• Practical experience deploying RAG architectures and combining structured and unstructured knowledge sources.
• Familiar with community-driven AI tools, libraries and repositories, including Hugging Face.
• T-shaped profile: deep expertise in one or two areas, backed by broad knowledge of AI/ML, cloud and consulting.
• Experience designing, building or integrating multi-agent systems and orchestration frameworks such as LangGraph, Semantic Kernel, Agent Framework, AutoGen or CrewAI, including agent protocols and coordination mechanisms.
• Knowledge of system optimization and security practices for scalable AI systems.

Conditions

Hybrid role, often on-site with customer teams.

Travel globally at least 25% of the time.

Equal opportunity employer that considers orientation, national origin, genetics, disability, age and veteran status, and follows fair employment practices on citizenship and immigration status.

Benefits

• Be Well programs supporting financial, mental, physical and social health.
• Certifications across Microsoft, Google and Amazon platforms, plus coaching and hands-on learning.
• Career paths from Junior to Principal, with tools to plan growth and regular feedback.
• Opportunities to lead engagements, help shape strategy and see your work deliver results.