Lead Ai Engineer

Lead AI EngineerAbout the RoleWe are looking for a Lead AI Engineer with deep expertise in LLM-based application development, RAG architectures, and scalable data engineering.
This is a hybrid role spanning both Data Science and Data Engineering, with responsibilities evenly split between hands-on technical work and people management.
The lead will participate in Agile ceremonies, daily standups, and product-focused meetings, while contributing individually to technical tasks and providing oversight for a team of 10 data engineers and data scientists.In addition to technical contributions, the role includes guiding project delivery, mentoring team members, and collaborating with stakeholders across Data Science, Product, and Engineering.
The ideal candidate has experience in both machine learning engineering and data infrastructure and is comfortable working in a fast-paced Agile environment.Key Responsibilities- Lead and mentor a 10-person team of AI Engineers and Data ScientistsAct as the main point of contact for Engineering, Product, and Data stakeholdersDefine architecture and design for LLM and RAG-based applicationsBuild and deploy applications leveraging LLMS (e.g., GPT, Claude, Sonnet)Design and implement RAG pipelines (chunking, ranking, vector search, and embedding strategies)Guide prompt engineering efforts – from design to evaluation using LLMsDrive hands-on development of scalable, production-grade ETL pipelinesEnsure adherence to best practices in containerization (Docker) and orchestration (Kubernetes)Own sprint planning, technical prioritization, and task delegation across the teamImplement CI/CD processes using tools such as Jenkins, Terraform, and AWS services (e.g., S3, Lambda)Evaluate and integrate vector databases (e.g., Solr, OpenSearch) into production applicationsMaintain a high standard of code quality, documentation, and operational readinessBalance leadership duties with individual contributions (approx.
50/50 split)Champion a collaborative, proactive, and agile engineering cultureRequired Qualifications- 10+ yoe of overall experience in engineering or data science roles.2–3 years of technical team leadership or people management experienceStrong proficiency in Python for scripting and data workflowsProduction experience with LLM architectures and RAG implementationsDeep understanding of prompt engineering and multi-step LLM prompt chainingExperience with SQL, NoSQL, and vector database technologies (Solr, OpenSearch)Solid AWS knowledge – especially S3 and LambdaFamiliarity with CI/CD pipelines, Terraform, and containerized deploymentExperience working cross-functionally and leading in Agile environmentsExcellent communication skills and the ability to translate business needs into technical solutionsNice-to-Have Skills:- Experience with LangChain, LlamaIndex, or other LLM orchestration frameworksExposure to Agentic RAG or LLM-based agent systemsFamiliarity with Big Data tools (Spark, Hadoop, EMR)Certifications in Cloud (AWS, Azure), AI/ML, or Data EngineeringOpen-source contributions or a demonstrable AI/ML project portfolio