01Responsibilities
Lead the technical design and end-to-end development of AI solutions that align with business goals and technical standards.
Design and build high-performing, scalable, enterprise-grade LLM/AI applications in cloud environments.
Mentor the development team, conduct code reviews, and implement engineering best practices.
Collaborate closely with AI Engineering and Data Science teams to deliver effective and efficient AI solutions.
Make architectural decisions regarding AI solutions and frameworks.
Define and implement best practices in GenAI/ML models lifecycle and ML operations/LLM operations.
Optimize AI models and systems, analyze performance, and scale solutions in cloud environments.
Gather technical requirements and estimate planned work.
Ensure code compliance with best practices in AI solutions development.
Create technical documentation.
Present solutions, concepts, and results to internal and external clients.
Must-Have Skills:
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 cooperative 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.
Nice-to-Have Skills & Knowledge:
Experience in designing and programming ML algorithms and data processing pipelines using Python.
Good understanding of CI/CD and DevOps concepts, with experience working with selected tools (preferably GitHub Actions, GitLab, or Azure DevOps).
Experience in productizing ML solutions using technologies like Spark/Databricks or Docker/Kubernetes. .
