AI/ML Engineer_MS

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
•