AI Engineer

We are seeking an innovative and results-driven AI Engineer to design, develop, and deploy artificial intelligence, machine learning, Generative AI, and full-stack solutions that solve real business challenges. The ideal candidate will have experience building AI-powered applications, training and integrating machine learning models, developing LLM and Generative AI solutions, building scalable web applications, and deploying enterprise solutions on cloud platforms.

The candidate should have strong hands-on experience in Python, AI/ML frameworks, Generative AI, LLMs, RAG, React, Node.js, NestJS/Express, MongoDB, APIs, databases, and cloud technologies, along with the ability to collaborate with cross-functional teams to deliver scalable, secure, and production-ready solutions.

Key Responsibilities

• Design, develop, and implement AI/ML models and intelligent applications.

• Build and optimize machine learning pipelines for training, testing, deployment, and monitoring.

• Develop and integrate Generative AI, Large Language Models (LLMs), AI-powered copilots, and intelligent assistants.

• Fine-tune, evaluate, and monitor AI models to ensure accuracy, reliability, performance, and cost efficiency.

• Implement Retrieval-Augmented Generation (RAG), prompt engineering, embeddings, semantic search, and vector database solutions.

• Develop and integrate AI solutions with enterprise applications through REST APIs and third-party services.

• Design and develop responsive front-end applications using React.js, JavaScript, and TypeScript.

• Develop scalable backend services and APIs using Node.js, Express.js, and NestJS.

• Design and integrate databases including MongoDB, SQL, PostgreSQL, and other NoSQL databases.

• Build end-to-end AI-powered applications by integrating AI models, APIs, frontend applications, backend services, and databases.

• Deploy AI and full-stack applications using cloud platforms such as Microsoft Azure, AWS, or Google Cloud.

• Work with cloud-based AI services, compute, storage, databases, networking, and security services.

• Implement containerized application deployments using Docker and Kubernetes.

• Build and maintain CI/CD pipelines for AI and application deployment.

• Implement MLOps practices for model versioning, deployment, monitoring, and lifecycle management.

• Ensure AI solutions comply with security, privacy, governance, and responsible AI standards.

• Monitor production systems, troubleshoot issues, and continuously improve application and model performance.

• Collaborate with business stakeholders, product managers, UI/UX designers, and engineering teams to identify AI use cases and deliver business solutions.

• Prepare technical designs, prototypes, proof-of-concepts, and architecture for AI-powered applications.

• Stay current with advancements in AI, machine learning, Generative AI, cloud technologies, and modern software engineering practices.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field.

• 1+ years of experience in AI/ML development, software engineering, Generative AI, or a related field.

• Strong programming skills in Python and experience with AI frameworks such as TensorFlow, PyTorch, or Scikit-learn.

• Strong knowledge of Generative AI technologies, LLMs, prompt engineering, RAG architectures, embeddings, and vector search.

• Good programming experience in JavaScript/TypeScript.

• Hands-on experience with React.js for developing modern web applications.

• Hands-on experience with Node.js and backend frameworks such as Express.js or NestJS.

• Experience working with APIs, databases, and cloud platforms.

• Experience with MongoDB and/or SQL-based databases.

• Understanding of MLOps practices, model deployment, monitoring, and CI/CD.

• Understanding of Docker and containerized application deployment.

• Strong analytical, problem-solving, debugging, and communication skills.

• Ability to work collaboratively with product, business, engineering, and DevOps teams.

Preferred Qualifications

• Experience with Azure AI Services, Azure OpenAI Service, Microsoft Copilot Studio, or Microsoft Fabric.

• Hands-on expertise with vector databases such as Pinecone, Weaviate, Azure AI Search, or similar technologies.

• Experience with LangChain, AI Agents, and Agentic AI architectures.

• Experience building enterprise AI copilots, intelligent assistants, recommendation systems, or automation solutions.

• Strong experience with React.js, Node.js, NestJS, Express.js, and MongoDB.

• Familiarity with containerization technologies such as Docker and Kubernetes.

• Experience with cloud platforms such as Microsoft Azure, AWS, or Google Cloud.

• Experience implementing enterprise AI governance and responsible AI practices.

• Experience with CI/CD pipelines and cloud-native application development.

• AI/ML or cloud certifications are a plus.

Technical Skills

Programming: Python, SQL, JavaScript, TypeScript

Frontend: React.js, HTML, CSS, TypeScript

Backend: Node.js, Express.js, NestJS

AI/ML Frameworks: PyTorch, TensorFlow, Scikit-learn

Generative AI: OpenAI, Azure OpenAI, LLMs, Prompt Engineering, RAG, Embeddings

Cloud Platforms: Microsoft Azure, AWS, Google Cloud

Databases: SQL, PostgreSQL, MongoDB, NoSQL, Vector Databases

Vector Databases: Pinecone, Weaviate, Azure AI Search

DevOps/MLOps: Git, Docker, Kubernetes, CI/CD Pipelines, Model Deployment, Model Monitoring

APIs & Integration: REST APIs, Third-Party APIs, AI APIs, Microservices

Security: OAuth, JWT, RBAC, API Security, Responsible AI

Success Measures:

• Delivery of scalable and production-ready AI and full-stack solutions that drive measurable business value.

• Improved process efficiency through AI automation and intelligent applications.

• Successful implementation of Generative AI, LLM, RAG, and AI-powered solutions.

• High model accuracy, application reliability, performance, and user satisfaction.

• Successful integration of AI capabilities with enterprise applications and existing systems.

• Efficient deployment and operation of AI applications across cloud environments.

• Secure, maintainable, and scalable application architecture.

• Effective implementation of security, governance, privacy, and responsible AI standards.

• Continuous improvement of model and application performance based on production feedback and business requirements.

Reporting to: Chief Technology Officer