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

Qualifications

Educational qualification:

Bachelor’s/Master’s degree in Computer Science, Engineering, or related field.

Experience :

4–6 Years