Senior AI Engineer

Role & responsibilities

Technical Skills

• 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.

Nice to Have

• Experience fine-tuning, evaluating, or optimizing open-source models.
• Experience with Databricks Mosaic AI, MLflow, or other enterprise AI development platforms.
• Knowledge of advanced AI techniques such as multi-agent systems, AI workflow orchestration, and autonomous agents.
• Experience with graph databases such as Neo4j and knowledge graph implementations.
• Familiarity with Deep Learning frameworks including PyTorch and TensorFlow.
• Experience building enterprise Copilots and conversational AI solutions.
• Azure AI Engineer Associate, Azure Developer Associate, Databricks, or equivalent cloud certifications.
• Understanding of Responsible AI, AI governance, and enterprise compliance requirements.