Senior AI Engineer

Responsibilities

• Design, build, validate, deploy, and maintain production-grade AI-enabled applications, intelligent agents, and reusable AI services.
• Develop LLM applications, agentic workflows, multi-agent systems, AI orchestration, prompt engineering, evaluation, guardrails, grounding, and observability capabilities.
• Build software applications, REST APIs, microservices, and event-driven architectures using secure and scalable engineering practices.
• Integrate structured and unstructured enterprise data, data pipelines, vector databases, semantic search, knowledge repositories, and embedding-based retrieval.
• Design enterprise-scale AI platforms and architectures aligned with business objectives, performance, scalability, reliability, and operational requirements.
• Deploy and operate AI workloads using Azure services, Docker, Kubernetes, MLOps, LLMOps, monitoring, and CI/CD pipelines.
• Establish engineering standards, reusable frameworks, governance, security, responsible AI, compliance, and best practices for AI solution delivery.
• Collaborate with product owners, data scientists, data engineers, architects, business stakeholders, users, and technical teams to gather requirements and deliver solutions.
• Lead technical initiatives, mentor engineering teams, communicate architecture decisions, and support continuous improvement across the software development lifecycle.

Requirements

• A college, university, or equivalent degree in Computer Science, Engineering, or a related subject, or relevant equivalent experience is required.
• At least 8 years of hands-on experience in software engineering, full-stack development, platform engineering, data engineering, or related technical fields.
• At least 3 years of experience designing, developing, or deploying AI, machine learning, or generative AI solutions.
• Strong experience building, deploying, and maintaining AI-powered applications, intelligent agents, production applications, and cloud solutions.
• Expertise in LLM application development, generative AI solution design, agent architectures, AI orchestration, RAG, semantic search, vector databases, and knowledge management architectures.
• Strong Python and software engineering experience, including object-oriented design, design patterns, testing, secure coding, APIs, microservices, distributed systems, and scalable application architecture.
• Experience with Azure AI services, Azure OpenAI, Azure Databricks, Spark, Azure Functions, Azure Kubernetes Service, Azure Storage, and related data services.
• Experience with Docker, Kubernetes, MLOps, LLMOps, model lifecycle management, monitoring, cloud deployment, and CI/CD platforms such as Git, Azure DevOps, or Jenkins.
• Experience designing enterprise AI platforms, evaluating AI technologies, optimizing model serving and inference, and establishing reusable engineering frameworks and standards.
• Strong analytical, problem-solving, communication, technical leadership, mentoring, decision-making, and customer-focused skills.
• Preferred experience includes fine-tuning or evaluating open-source models, Databricks Mosaic AI, MLflow, Neo4j, knowledge graphs, PyTorch, TensorFlow, enterprise copilots, conversational AI, and Azure or Databricks certifications.