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

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  Design and implement ML and GenAI solutions including RAG pipelines, LLM integrations, prompt engineering, and evaluation/guardrail frameworks.

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  Develop and deploy API-based AI applications using FastAPI, Flask, or Plotly Dash.

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  Build end-to-end ML pipelines: data ingestion, feature engineering, model training, validation, deployment, and monitoring.

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  Work with cross-functional teams to translate business needs into AI-driven outcomes.

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  Deploy workloads using Azure App Service, Cloud Run , Azure Bot Service, Dialogflow, and other cloud-native platforms.

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  Implement MLOps workflows for CI/CD, model registry, experiment tracking, and automated retraining.

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  Build and optimize ETL/ELT pipelines using Azure Data Factory, BigQuery, Databricks, and other data engineering tools.

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  Create dashboards and analytical insights using Power BI, Tableau, Looker, QuickSight, or ThoughtSpot.

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  Ensure scalable, secure, and cost-optimized deployment across Azure/GCP/AWS environments.

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Required Technical Skills

Programming & Languages

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  Python (advanced), SQL (strong), HTML/CSS/JavaScript (working knowledge)

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LLMs & GenAI

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  LangChain, LangGraph

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  Google ADK, Vertex AI, AWS Bedrock

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  RAG architectures, embeddings, vector retrieval

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  Prompt design, evaluation metrics, guardrails/security

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  Azure AI Foundry, Azure OpenAI, Azure AI Search, Azure Document Intelligence

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  Custom model development using GPT, LangChain, and relevant frameworks

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  Prompt engineering, LogProbs handling, vector search integrations

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Data Engineering & Platforms

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  BigQuery, Azure Synapse, Azure Data Factory, Databricks

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  Blob Storage, Cloud Storage, Document AI

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  Strong understanding of ETL/ELT, feature engineering & data profiling

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  Event-driven architecture and streaming systems for agentic workflows

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  Data ingestion, transformation, and vector database management

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  Ensuring data quality, lineage, governance, and observability

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BI & Analytics

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  Power BI, Tableau, Looker, ThoughtSpot, QuickSight

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DevOps & MLOps

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  Docker, CI/CD pipelines

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  Model deployment & monitoring

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  Vertex AI Agent Engine, model registry, experiment tracking

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Educational qualification:

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

Experience :

4–6 Years