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