Must have

• genAI

• Python

• SQL

• English (C1)

Requirements description

• 10+ years of experience in AI/software engineering, including a minimum of 4 years in a Tech Lead role.
• At least 6+ years of experience in production-ready Python AI-related code development.
• At least 2+ years of experience in production-ready LLM-related code development, preferably based on the Retrieval-Augmented Generation (RAG) concept.
• Advanced knowledge of AI frameworks and experience leading AI projects from design through to implementation.
• Strong analytical and problem-solving skills with the ability to optimize AI solutions for diverse applications.
• Strong knowledge and experience in Generative AI, including LLMs, chatbots, AI agents, and RAG mechanisms.
• Deep understanding of LLM evaluators, validators, and guardrails.
• Proficiency in LLMOps concepts such as GenAI operationalization and scaling (e.g., LLMs serving, performance & API Gateways, LLMs tracking & monitoring).
• Excellent knowledge of Python, SQL, and GenAI frameworks (e.g., LangChain, LangGraph, vector databases).
• Experience in building and deploying production-ready systems based on microservices.
• Solid understanding of ML/AI concepts: types of algorithms, machine learning frameworks, model efficiency metrics, model lifecycle, AI architectures.
• Proven ability to collaborate effectively across technical and non-technical teams.
• Familiarity with cloud environments such as Azure (preferred), GCP, or AWS, including AI-related managed services.
• Familiarity with CI/CD, testing, and containerized deployments.
• A collaborative approach to leadership, with experience mentoring and guiding diverse technical teams.
• Excellent communication skills in English, with the ability to convey complex technical concepts to various audiences.

Offer description

We are looking for a highly skilled and experienced GenAI Expert/Tech Lead with solid expertise in Generative AI (GenAI) to guide our teams & projects in building and implementing advanced systems based on Large Language Models (LLMs), chatbots, AI agents, and Retrieval-Augmented Generation (RAG) mechanisms. As a GenAI Expert/Tech Lead, you will be responsible for designing, implementing, and optimizing GenAI solutions, as well as mentoring the engineering team. Your knowledge and experience will be crucial in making architectural decisions, selecting technologies, and implementing best practices in AI-driven development.

We offer:

• Stable employment. On the market since 2008, 1500+ talents currently on board in 7 global sites.
• Workation. Enjoy working from inspiring locations in line with our workation policy.
• Great Place to Work® certified employer.
• Flexibility regarding working hours and your preferred form of contract.
• Comprehensive online onboarding program with a “Buddy” from day 1.
• Cooperation with top-tier engineers and experts.
• Unlimited access to the Udemy learning platform from day 1.
• Certificate training programs. Lingarians earn 500+ technology certificates yearly.
• Upskilling support. Capability development programs, Competency Centers, knowledge sharing sessions, community webinars, 110+ training opportunities yearly.
• Grow as we grow as a company. 76% of our managers are internal promotions.
• A diverse, inclusive, and values-driven community.
• Autonomy to choose the way you work. We trust your ideas.
• Create our community together. Refer your friends to receive bonuses.
• Activities to support your well-being and health.
• Social fund benefits for everyone. All Lingarians can apply for social fund benefits, such as vacation co-financing.
• Plenty of opportunities to donate to charities and support the environment.
• Modern office equipment. Purchased for you or available to borrow, depending on your location.

Your responsibilities

1. Lead the technical design and end-to-end development of AI solutions that align with business goals and technical standards.
2. Design and build high-performing, scalable, enterprise-grade LLM/AI applications in cloud environments.
3. Mentor the development team, conduct code reviews, and implement engineering best practices.
4. Collaborate closely with AI Engineering and Data Science teams to deliver effective and efficient AI solutions.
5. Make architectural decisions regarding AI solutions and frameworks.
6. Define and implement best practices in GenAI/ML models lifecycle and ML operations/LLM operations.

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