Modernization Engineer

Description

As a Senior Forward Deployed Engineer, you will collaborate directly with customers to solve complex challenges and deliver AI solutions that create measurable business value. This hybrid engineering and consultative role operates within a delivery model that brings together engineering, consulting, and product expertise, with accountability for outcomes as well as code.

You will scope, prototype, and deploy tailored solutions in dynamic customer environments, often on-site. The work includes Generative AI, Agentic AI architectures, and multi-agent ecosystems; deployment insights are fed back into platforms and frameworks to accelerate future implementations.

Key Responsibilities

• Build autonomous agents using LLMs, planning algorithms, and decision-making frameworks.
• Implement agent architectures that support autonomy, interactivity, and task completion.
• Integrate agents into applications, APIs, and workflows, including copilots, chatbots, and automation tools.
• Connect agents to external services through APIs, databases, and cloud platforms.
• Tune agent behavior using feedback loops, reinforcement learning, semantic knowledge layers, and user interaction.
• Monitor performance and implement safety, reliability, and guardrail mechanisms.
• Work cross-functionally with researchers, engineers, and product teams.
• Maintain clear documentation of agent logic, design decisions, and dependencies.
• Build and maintain an Enterprise Agents and Tools Registry for metadata and lifecycle management.
• Implement an Agent Communication Gateway with security, rate limits, observability, and cost controls.
• Use AI-agent orchestration patterns and workflow orchestration engines, such as Temporal and Airflow.
• Ensure agent-level security, including authentication, authorization, and data protection.
• Optimize cost, scalability, performance, and reliability across cloud and on-premises environments.
• Use knowledge graphs for agent reasoning and data integration.
• Deploy and customize agentic AI platforms, including LLM agents and orchestration frameworks.
• Integrate AI systems with enterprise APIs, data platforms, and workflows.
• Resolve technical blockers involving data ingestion, model deployment, and agent behavior.
• Design and refine prompts for clarity, compliance, and contextual accuracy.
• Translate business logic into agentic workflows and task trees.
• Implement observability tools for reliability, latency, and trustworthiness.
• Maintain performance metrics and feedback loops for continuous improvement.
• Build and iterate custom AI solutions tailored to customer needs using agentic AI frameworks.
• Own delivery end to end, from scoping through production, and engineer solutions that drive adoption and measurable business outcomes.
• Contribute feedback and code to the evolution of AI platforms in collaboration with product teams.
• Partner with customers to translate business challenges into high-quality technical solutions.
• Troubleshoot and improve system performance, scalability, and reliability.
• Capture deployment learnings, document best practices, and share insights to improve platforms and frameworks.
• Adapt to emerging technologies and evolving customer requirements through continuous professional development.

Career Opportunities

This role offers vertical and horizontal career paths, including opportunities to progress toward enterprise architect, chief architect, or distinguished engineer roles. Travel opportunities may be available.

Benefits

• Flexible working policy, including home-working options depending on the position.
• Private health card.
• Life insurance package.
• Multisport Card.
• Employee discounts.
• Benefit options for parents and children.
• Sports activities and team events.
• Free language courses and personal-development opportunities.
• Employee referral program.
• Mindfulness and yoga classes.
• BeWell Program with online consultants for law, tax, life events, psychology, and coaching.
• Retirement pension plan.
• Cafeteria platform with benefits points.

Job Qualifications

Required Skills and Experience

• Bachelor’s degree in Computer Science, Engineering, or equivalent.
• Hands-on Python development and frontend UI experience, including TypeScript and React.js, to build demos and systems from scratch as a full-stack engineer.
• Hands-on experience building AI-based solutions with frameworks such as LangChain, Microsoft Semantic Kernel, Google ADK, or Microsoft Agent Framework.
• Knowledge of LLMs, AI-agent architectures, and agent telemetry/observability frameworks, such as LangSmith, Langfuse, and LiteLLM.
• Expertise with Docker, Kubernetes, and at least one cloud platform—Azure, AWS, or GCP—or on-premises environments.
• Experience with microservices-based architectures.
• Solid understanding of the software delivery lifecycle, version control with Git and GitHub, and data-engineering tools such as Pandas and Spark.
• Experience with cloud AI platforms and distributed-computing architectures.
• Ability to translate business requirements into technical solutions and communicate technical value to diverse stakeholders, including executives.
• Hands-on SQL, NoSQL, and vector database experience for agent data storage, semantic queries, and retrieval; examples include PostgreSQL and MongoDB.
• Proficiency in CI/CD, such as GitHub Actions, automated testing, and observability.
• Familiarity with agent-based modeling, multi-agent systems, or reinforcement learning.
• Proficiency in API development, backend services, and cloud platforms.
• Practical knowledge of deploying RAG architectures and integrating structured and unstructured knowledge sources into AI solutions.
• Familiarity with open-source AI tools and libraries, including Hugging Face.
• Deep technical expertise in one or two domains and broad understanding across AI/ML, cloud, and consulting.
• Experience designing, building, or integrating multi-agent systems and orchestration frameworks, including LangGraph, Semantic Kernel, Agent Framework, AutoGen, and CrewAI; this includes developing agent protocols and coordination mechanisms.
• Knowledge of system-level optimization and security best practices for scalable AI systems.
• Willingness to travel up to 25% globally.