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

Educational qualification

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

Experience

4–6 Years