AI Engineering & Generative AI

Role & responsibilities

• 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.
  

Experience

• Minimum 8+ years of hands-on experience in Software Engineering, Full Stack Development, Platform Engineering, Data Engineering, or related technical fields.
• Minimum 3+ years of experience designing, developing, or deploying AI, Machine Learning, or Generative AI solutions.
• Demonstrated success transitioning enterprise applications from traditional software architectures to AI-enabled solutions.
• Proven experience building and deploying scalable production applications in cloud environments.
• Experience integrating AI capabilities into business applications, workflows, and enterprise platforms.
• Experience working with modern AI frameworks, LLMs, and agent-based application architectures.
• Experience operating within Agile software development environments.
• Experience collaborating with product managers, data scientists, architects, and business stakeholders to deliver innovative solutions.
• Proven ability to lead technical initiatives and mentor engineering teams.