Senior AI Engineer

AI Engineering & Generative AI

Strong experience building, deploying, and maintaining AI-powered applications and intelligent agents.

Expertise in Large Language Model (LLM) application development and Generative AI solution design.

Experience developing agentic AI workflows, multi-agent systems, and AI orchestration frameworks.

Knowledge of prompt engineering, agent evaluation, guardrails, grounding techniques, and AI application observability.

Familiarity with Retrieval-Augmented Generation (RAG), semantic search, vector databases, and knowledge management architectures.

Open Source AI Frameworks

Hands-on experience with modern AI orchestration frameworks including LangGraph, LangChain, LlamaIndex Hugging Face, Open-Source LLM ecosystems

Experience integrating both commercial and open-source foundation models.

Knowledge of model serving, inference optimization, and AI workflow orchestration patterns.

Software Engineering & Application Development

Strong proficiency in Python with experience building production-grade applications and APIs.

Experience with modern software engineering practices including object-oriented design, design patterns, testing, and secure coding principles.

Experience developing REST APIs, microservices, and event-driven architectures.

Familiarity with front-end technologies such as React, Angular, or similar frameworks for AI application development.

Understanding of distributed systems and scalable application architecture.

Data Engineering & AI Platforms

Experience working with Azure Databricks, Spark, and modern data platforms supporting AI workloads.

Strong understanding of data pipelines, feature engineering, and data preparation for AI applications.

Experience integrating enterprise data sources, structured and unstructured datasets, and knowledge repositories.

Familiarity with vector databases and embedding-based retrieval architectures.

Understanding of modern data lake, warehouse, and lakehouse architectures.

Cloud, DevOps & MLOps

Strong experience with Azure cloud services supporting AI workloads, including:

Azure AI Services

Azure OpenAI

Azure Databricks

Azure Functions

Azure Kubernetes Service (AKS)

Azure Storage and Data Services

Experience with Docker, Kubernetes, and cloud-native application deployment.

Familiarity with MLOps and LLMOps practices including model lifecycle management, monitoring, and continuous deployment.

Experience implementing CI/CD pipelines using Git, Azure DevOps, Jenkins, or equivalent platforms.

Knowledge of AI governance, security, responsible AI, and compliance practices.

AI Solution Architecture

Experience designing enterprise-scale AI platforms and reusable AI services.

Ability to evaluate AI technologies and recommend architecture patterns aligned with business objectives.

Knowledge of AI system performance optimization, scalability, reliability, and operational excellence.

Experience establishing engineering standards, reusable frameworks, and best practices for AI solution delivery.