AI/ML Engineer_MS

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.

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

Requirements

Bachelor’s/Master’s degree in Computer Science, Engineering, or related field; 4–6 years of experience; Python (advanced); SQL (strong); HTML/CSS/JavaScript (working knowledge); 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; relevant frameworks; prompt engineering; LogProbs handling; vector search integrations; 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; vector database management; data quality; lineage; governance; observability; Power BI; Tableau; Looker; ThoughtSpot; QuickSight; Docker; CI/CD pipelines; model deployment and monitoring; Vertex AI Agent Engine; model registry; experiment tracking