AI/ML Engineer_MS

Job Description We are seeking an experienced  AI/ML Engineer (4–6 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