SeniorData/MLEngineer

## Accountabilities Design, build, and maintain scalable end-to-end data pipelines for ingestion, transformation, and delivery across data platforms and ML systems. Develop workflows for structured and unstructured data processing, enabling semantic search and advanced retrieval capabilities. Architect and implement analytics and BI solutions with AI-driven and natural language query functionalities. Define and support prompt engineering strategies, orchestration workflows, and model fine-tuning processes for LLM-based applications. Manage and optimize vector databases and indexing strategies for retrieval-augmented generation (RAG) systems. Collaborate with BI, engineering, and business teams to translate requirements into scalable data and ML solutions. Ensure documentation of data workflows, pipelines, and model deployment processes. Stay up to date with advancements in data engineering, MLOps, and LLM ecosystems. Requirements: 8+ years of experience in Data Engineering, including at least 2 years focused on MLOps or ML-driven systems. Strong proficiency in Python for data processing, transformation, and large-scale data engineering tasks. Deep understanding of vector databases, semantic search, and RAG architectures. Experience integrating LLM frameworks into production data workflows (training, fine-tuning, and inference). Hands-on experience with cloud platforms such as AWS or Azure, including ML/AI services. Strong experience with distributed data processing tools such as Apache Spark, Hadoop, and Kafka. Solid knowledge of SQL/PLSQL and data warehouse technologies (Snowflake, Redshift, or similar). Experience designing complex data pipelines from multiple sources (APIs, RDBMS, JSON, flat files). Strong understanding of software engineering principles and experience working in Agile environments. Proficiency with Git and collaborative development workflows. Strong communication skills in English (written and spoken). Nice to have: experience with LangChain, Ll