Building high-performing, scalable, enterprise-grade LLM/AI applications in cloud environment
Working with Data Science teams to analyze requirements, build architecture conceptions, lead a implementation GenerativeAI and Machine Learning models into production (Tech Lead)
Practical and innovative implementations of LLM/ML/AI automation, for scale and efficiency
Design, delivery and management of industrialized processing pipelines
Defining and implementing best practices in GenAI/ML models life cycle and ML operations/LLM operations
Implementing AI /MLOps/LLMOps frameworks and supporting Data Science teams in best practices
Gathering and applying knowledge on modern techniques, tools and frameworks in the area of ML Architecture and Operations
Gathering technical requirements & estimating planned work
Presenting solutions, concepts and results to internal and external clients
Creating technical documentation including diagrams
Requirements:
At least 8+ years of experience in production-ready Python code development (e.g., microservices, APIs, etc.)
At least 8+ years of experience in production-ready ML-related code development
At least 2 years of experience in production-ready LLM-related code development, preferably based on the Retrieval Augmented Generation concept (RAG)
At least 3+ years of experience in Cloud Architecture
Good understanding and experience with GenerativeAI models APIs (Large Language Models/Large Multimodal Models)
Good understanding and experience with LLM orchestrators (e.g., Langchain, etc.) and concepts (RAG, in-context learning, fine-tuning)
Good understanding of LLM evaluators, validators, and guardrails
Good understanding of LLMOps concepts like GenAI operationalization\scaling (e.g., LLMs serving, performance & API Gateways, LLMs tracking & monitoring)
Experience in developing GenAI apps in rapid frameworks (e.g., Streamlit)
Experience in MLOps/LLMOps tools like AzureML/AzureAI or GCP VertexAI
Good understanding of ML/AI concepts: types of algorithms, machine learning frameworks, model efficiency metrics, model life-cycle, AI architectures
Good understanding of Cloud concepts and architectures, as well as working knowledge with selected cloud services, preferably Azure or GCP
UML notation
Good communication skills
Ability to work in a team and support others
Taking responsibility for tasks and deliverables
Great problem-solving skills and critical thinking
Fluency in written and spoken English.
Nice to have skills & knowledge:
Experience in designing, programming ML algorithms, and data processing pipelines using Python
Good understanding of CI/CD and DevOps concepts, and experience in working with selected tools (preferably GitHub Actions, GitLab, or Azure DevOps)
Experience in productizing ML solutions using technologies like Spark/Databricks or Docker/Kubernetes.
BPMN, Archimate
We offer:
Stable employment. On the market since 2008, 1500+ talents currently on board in 7 global sites.
“Office as an option” model. You can choose to work remotely or in the office.
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.
Internal Gallup Certified Strengths Coach to support your growth.
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.
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.
