AI/ML Engineer at BoschGroup

Job Description We are seeking an experienced AI/ML Engineer (5–8 years) with strong hands-on expertise in end-to-end machine learning, GenAI solution development, data engineering, and cloud-native deployment. The role involves building scalable AI systems, designing LLM-based applications, and integrating enterprise-grade MLOps pipelines across any one of Azure, GCP, and AWS environments. Key Responsibilities • Design and implement ML and GenAI solutions including RAG pipelines, LLM integrations, prompt engineering, and evaluation/guardrail frameworks. • Develop and deploy API-based AI applications using FastAPI, Flask, or Plotly Dash. • Build end-to-end ML pipelines: data ingestion, feature engineering, model training, validation, deployment, and monitoring. • Work with cross-functional teams to translate business needs into AI-driven outcomes. • Deploy workloads using Azure App Service, Cloud Run, Azure Bot Service, Dialogflow, and other cloud-native platforms. • Implement MLOps workflows for CI/CD, model registry, experiment tracking, and automated retraining. • Build and optimize ETL/ELT pipelines using Azure Data Factory, BigQuery, Databricks, and other data engineering tools. • Create dashboards and analytical insights using Power BI, Tableau, Looker, QuickSight, or ThoughtSpot. • Ensure scalable, secure, and cost-optimized deployment across Azure/GCP/AWS environments. Required Technical Skills Programming & Languages • Python (advanced), SQL (strong), HTML/CSS/JavaScript (working knowledge) LLMs & GenAI • LangChain, LangGraph • Google ADK, Vertex AI, AWS Bedrock • RAG architectures, embeddings, vector retrieval • Prompt design, evaluation metrics, guardrails/security • Azure AI Foundry, Azure OpenAI, Azure AI Search, Azure Document Intelligence • Custom model development using GPT, LangChain, and relevant frameworks • Prompt engineering, LogProbs handling, vector search integrations Data Engineering & Platforms • BigQuery, Azure Synapse, Azure Data Factory, Databricks • Blob Storage, Cloud Storage, Document AI • Strong understanding of ETL/ELT, feature engineering & data profiling • Event-driven architecture and streaming systems for agentic workflows • Data ingestion, transformation, and vector database management • Ensuring data quality, lineage, governance, and observability BI & Analytics • Power BI, Tableau, Looker, ThoughtSpot, QuickSight DevOps & MLOps • Docker, CI/CD pipelines • Model deployment & monitoring • Vertex AI Agent Engine, model registry, experiment tracking