AIML Engineer

Job Description

We are seeking an experienced AI/ML Engineer (58 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

Qualifications :

Educational qualification:

Bachelors/Masters degree in Computer Science Engineering or related field.

Experience :

58 Years

Remote Work :

No

Employment Type :

Full-time