ML Engineer (NLP/GenAI)

About the role

As part of the product team, you will collaborate with product owners, ML engineers, application developers, and business SMEs to develop and scale GenAI and agent-based capabilities within digital products. This role focuses on active development, learning, and contributing to valuable AI solutions.

Responsibilities

• Contribute to the design, development, and deployment of GenAI and agentic systems supporting reasoning, planning, and semi-autonomous workflows.
• Build and enhance components of GenAI solutions using LLMs, RAG pipelines, prompt engineering, and tool integration.
• Develop intelligent workflows using techniques such as prompt engineering, context orchestration, and function/tool calling.
• Integrate GenAI capabilities into enterprise applications using APIs, microservices, and containerized environments.
• Collaborate with senior AI engineers to implement scalable, reliable GenAI systems and follow established design patterns and standards.
• Participate in end-to-end delivery of GenAI initiatives, contributing to development, testing, and deployment.
• Continuously learn and adopt best practices in GenAI, NLP, and agentic systems development.

Requirements

• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or similar specialization.
• 6+ years of experience in AI/ML, software engineering, data, or analytics, focusing on digital solutions development.
• 2-5 years of core experience in AI/ML solution development, ML engineering, with a focus on NLP and applied GenAI systems.
• Practical experience with LLM ecosystems (e.g., OpenAI, Azure OpenAI, open-source models), including prompt engineering and basic context design.
• Practical experience with RAG architecture, embeddings, or vector databases.
• Experience building and integrating applications using APIs, microservices, or containerized environments.
• Familiarity with software engineering best practices (version control, testing, CI/CD basics).
• Exposure to agentic workflows, including tool usage, chaining, or multi-step reasoning.
• Familiarity with LLM evaluation concepts and basic understanding of LLMOps practices such as monitoring, versioning, and cost awareness.

Skills

• LLM Systems Engineering: Ability to contribute to building scalable and reliable GenAI systems with a focus on performance and maintainability. Understanding of standard design patterns and engineering practices for LLM-based applications. Familiarity with deploying and integrating GenAI solutions into production environments.
• GenAI & Agentic Solution Development: Practical experience in developing GenAI and agent-based solutions for structured workflows and assisted decision-making. Working knowledge of techniques such as RAG, prompt engineering, and tool integration. Ability to implement intelligent workflows combining human-in-the-loop and automated processes under guidance.
• Foundational AI/ML & Software Engineering: Solid foundation in ML concepts and software engineering principles for building maintainable systems. Experience developing and integrating services using APIs, microservices, and modern engineering practices. Proficiency in leveraging AI-assisted development tools (e.g., GitHub Copilot, Claude Code) to improve productivity and code quality.
• Global Collaboration & Enablement: Ability to collaborate effectively within distributed teams across geographies. Solid teamwork skills working with senior engineers and cross-functional stakeholders. Demonstrates a continuous learning mindset with interest in GenAI, NLP, and agentic systems.