Senior AI Engineer

Job description

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.