Role Overview
We are seeking a highly skilled and innovative AI Engineer to design, develop, and deploy AI-powered applications and intelligent digital products. In this role, you will build scalable, secure, and user-centric solutions powered by Large Language Models (LLMs), machine learning, and cloud-native technologies, with a primary focus on AI services, agent workflows, and backend systems.
The ideal candidate has strong expertise in modern software engineering combined with hands-on experience integrating AI capabilities into enterprise applications. You should be comfortable building scalable APIs, AI orchestration pipelines, agent workflows, and cloud deployments while collaborating in agile, fast-paced engineering teams.
Key Responsibilities
- Design, develop, and maintain end-to-end AI-powered applications and platforms.
- Develop scalable backend services and APIs using Python, FastAPI, Node.js, or similar technologies.
- Integrate LLMs, AI agents, RAG pipelines, vector databases, and AI orchestration frameworks into enterprise applications.
- Build agent workflows—including multi-step reasoning, tool use, memory, planning, and human-in-the-loop patterns—using frameworks such as LangGraph, CrewAI, AutoGen, Semantic Kernel, or similar.
- Contribute to user-facing experiences by building or extending web interfaces with frameworks such as React or Next.js when required.
- Collaborate with product managers, designers, data scientists, and business stakeholders to translate requirements into technical solutions.
- Develop reusable backend modules, AI integration services, and (where relevant) UI components to accelerate product delivery.
- Optimize applications for performance, scalability, reliability, and security.
- Implement authentication, authorization, API security, and secure system integrations.
- Work with relational and non-relational databases such as PostgreSQL, Redis, MongoDB, Elasticsearch, or Neo4J.
- Build and manage CI/CD pipelines, automated testing, and deployment workflows.
- Deploy and manage applications using Docker, Kubernetes, and cloud platforms preferably but not limited to Azure.
- Utilize AI-assisted development tools and engineering workflows to improve productivity, code quality, and delivery speed.
- Troubleshoot production issues, monitor application health, and ensure high availability of AI systems.
- Participate in code reviews, architecture discussions, and engineering best practices initiatives.
Qualifications
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Software Engineering, or a related field.
- 3+ years of experience in software development, including hands-on experience building AI-enabled applications.
- Strong backend development experience using Python, FastAPI, Flask, Django, Node.js, or Express.js.
- Hands-on experience integrating Generative AI and Large Language Models (LLMs) into applications.
- Experience designing and building agent workflows, including tool use, multi-step reasoning, planning, and memory, using frameworks such as LangGraph, CrewAI, AutoGen, Semantic Kernel, or similar.
- Familiarity with vector databases, embeddings, and Retrieval-Augmented Generation (RAG) architectures.
- Experience working with REST APIs, GraphQL, microservices, and distributed systems architectures.
- Experience with relational and NoSQL databases.
- Strong understanding of cloud-native development and containerization technologies such as Docker and Kubernetes.
- Experience with Git, CI/CD pipelines, automated testing, and agile development methodologies.
- Strong problem-solving, debugging, and analytical skills.
- Excellent communication and collaboration skills in cross-functional environments.
Nice to Have
- Working knowledge of frontend technologies such as React, Next.js, TypeScript, HTML5, or CSS3.
- Experience building AI copilots, enterprise chatbots, or autonomous AI workflows.
- Familiarity with workflow automation platforms such as n8n, Dify, Flowise, LangFlow, or similar tools.
- Experience with AI orchestration frameworks such as LangChain, LlamaIndex, Semantic Kernel, or similar technologies.
- Experience with multimodal AI applications involving text, image, audio, or video processing.
- Exposure to MLOps practices including model deployment, monitoring, and lifecycle management.
- Knowledge of event-driven architecture and message brokers such as Kafka or RabbitMQ.
- Experience implementing real-time applications using WebSockets or streaming technologies.
- Familiarity with authentication protocols such as OAuth2, OpenID Connect, and JWT.
- Understanding of AI governance, responsible AI, and model evaluation practices.
- Experience with infrastructure-as-code tools such as Terraform.
- Contributions to open-source projects or technical communities.
- Experience using AI coding assistants and productivity tools in software engineering workflows.
