AI Development Team Lead
Unilink Ltd. · Ramat Gan, Tel Aviv District, Israel
Apply & track with Apply EdgeWe are hiring: AI Development Team LeadWe are looking for an experienced AI Development Team Lead to lead and shape the organization’s AI capabilities.Key ResponsibilitiesLead, mentor, and develop a team of AI engineers and developers, driving technical excellence and professional growth.Design scalable architectures for enterprise AI and Generative AI solutions.Lead the development of AI Agents, RAG solutions, intelligent automation, and agentic workflows.Collaborate closely with Business, IT, Data, Information Security, and Infrastructure teams.Establish development standards, conduct code reviews, and promote engineering best practices.Own the quality, performance, security, scalability, and reliability of AI solutions.Evaluate and adopt emerging AI technologies based on organizational needs and strategic priorities.Drive innovation initiatives and continuously develop the team’s expertise in advanced AI technologies.Lead AI projects end-to-end, from requirements and architecture through development, deployment, and production.Required Qualifications5+ years of professional software development experience - Must3+ years of experience managing software development teams - Must2+ years of proven experience developing AI and Generative AI solutions in production environments - MustStrong proficiency in Python and backend development - MustHands-on experience with LLMs and platforms such as OpenAI, Azure OpenAI, Google Vertex AI, or AWS Bedrock - MustExperience with LangChain, LangGraph, LlamaIndex, or similar frameworks - MustHands-on experience with AI Agents, MCP, RAG, Prompt Engineering, Function Calling, and Agentic Workflows - MustExperience with Vector Databases such as Pinecone, Weaviate, Milvus, or pgvector - MustExperience with Microservices, Docker, Kubernetes, and CI/CD pipelines - MustExperience working with cloud environments such as AWS, Azure, or GCP - MustStrong experience with Git and Agile methodologies - Must