: AI Engineer – AI Integration & Application Development

Company: AWS Geek Pvt. Ltd.

Department: Engineering & Product Development

Location: Panchkula, Haryana, India

Job Type: Full-Time

Work Mode: Work From Office

About the Role

AWS Geek is looking for a skilled and innovative AI Engineer who can design, develop, integrate, and deploy AI-powered solutions across our web applications, mobile applications, and digital platforms.

The candidate will be responsible for implementing AI capabilities throughout the application ecosystem, including AI chatbots, intelligent assistants, workflow automation, recommendation systems, document processing, and other AI-powered features.

We are looking for someone who understands both AI engineering and software development and can take an AI feature from initial concept to production. The ideal candidate should be comfortable working with existing applications, integrating third-party AI services, developing custom AI solutions, and collaborating with developers to build scalable and reliable products.

Key Responsibilities

1. AI Integration & Application Development

- Design and implement AI-powered features for websites, web applications, and mobile applications.
- Integrate AI models and third-party AI APIs into existing and new applications.
- Build intelligent features that improve user experience, automate processes, and solve real business problems.
- Develop backend services and APIs to connect AI functionality with application frontends and databases.
- Work closely with frontend and backend developers to ensure seamless AI integration.

1. Large Language Models (LLMs) & Generative AI

- Work with LLMs and generative AI platforms such as OpenAI, Anthropic, and Google Gemini.
- Develop AI assistants, conversational interfaces, and context-aware chatbots.
- Implement prompt engineering, structured outputs, tool calling, and function calling.
- Build Retrieval-Augmented Generation (RAG) systems that allow AI applications to retrieve and use relevant information from company documents and databases.
- Evaluate and integrate suitable models based on accuracy, speed, cost, and project requirements.

1. AI Agents & Workflow Automation

- Develop AI agents capable of executing tasks through approved tools, APIs, and application workflows.
- Build automated workflows for customer support, data processing, lead management, reporting, and other business operations.
- Integrate AI with existing software systems, databases, and third-party platforms.
- Implement appropriate permissions, human approval steps, and safeguards for sensitive or consequential actions.
- Explore agent frameworks and orchestration tools where they provide a practical advantage.

1. Machine Learning & Custom AI Solutions

- Identify opportunities to use machine learning, predictive analytics, classification, and recommendation systems.
- Develop, fine-tune,



or adapt AI models when required by project needs.
- Prepare and process datasets for model development and evaluation.
- Evaluate model performance and improve the accuracy and reliability of AI-driven features.
- Determine when to use an existing model or API versus building a custom solution.

1. Backend, APIs & Database Integration

- Develop AI-related backend services using Python and frameworks such as FastAPI or Flask.
- Integrate REST APIs, webhooks, databases, and external services.
- Work with SQL and NoSQL databases as required.
- Implement vector databases and embedding-based retrieval for semantic search and RAG applications.
- Optimize API response times, concurrent requests, resource usage, and system reliability.

1. Deployment, Testing & Maintenance

- Deploy AI services to suitable cloud infrastructure and production environments.
- Collaborate with DevOps and development teams on CI/CD, containerization, and application deployment.
- Monitor AI performance, latency, token usage, infrastructure costs, and failures.
- Implement logging, error handling, testing, and monitoring for AI-powered features.
- Troubleshoot issues and continuously improve deployed AI functionality.

1. Security, Privacy & Responsible AI

- Protect sensitive data, API credentials, and application information.
- Implement appropriate authentication, authorization, and data access controls.
- Identify and mitigate risks such as prompt injection, data leakage, unsafe tool execution, and unreliable AI outputs.
- Apply appropriate safeguards, validation, and human oversight to AI-powered workflows.
- Follow applicable data protection requirements and secure development practices.

1. Research & Innovation

- Stay updated on new AI models, frameworks, APIs, and development tools.
- Evaluate emerging technologies and recommend suitable solutions for company products.
- Build proof-of-concept projects and prototypes to validate new ideas.
- Document AI architectures, integrations, deployment processes, and technical decisions.
- Recommend improvements that make AI features more useful, reliable, scalable, and cost-effective.

Required Technical Skills
- Strong programming skills in Python.
- Experience integrating LLMs and generative AI APIs into real applications.
- Understanding of LLMs, prompt engineering, embeddings, vector search, and RAG.
- Experience building APIs and backend services.
- Knowledge of REST APIs, authentication, JSON, and database integration.
- Understanding of AI agents, tool calling, and workflow automation.




- Familiarity with SQL databases and at least one vector database.
- Knowledge of Git, debugging, testing, and deployment workflows.
- Understanding of application security, data privacy, and AI reliability.

Preferred Skills Experience with some of the following technologies will be an advantage:
- AI platforms: OpenAI API, Anthropic API, Google Gemini.
- AI frameworks: LangChain, LlamaIndex, or comparable tools.
- Agent development: Tool calling, agent orchestration, and multi-step workflows.
- Vector databases: Pinecone, Qdrant, Weaviate, or pgvector.
- Backend development: FastAPI, Flask, Django, or Node.js.
- Machine learning: PyTorch, TensorFlow, scikit-learn, or Hugging Face.
- Cloud infrastructure: AWS, Google Cloud, or Azure.
- Deployment: Docker, CI/CD, and production monitoring.
- Frontend integration: JavaScript/TypeScript, React, or Next.js.
- Mobile integration: Experience integrating backend AI services into Android, iOS, or cross-platform mobile applications.

Candidates do not need to know every framework listed. Solid fundamentals, practical implementation experience, and the ability to learn new technologies are more important.

Required Qualifications

- Bachelor's degree in Computer Science, Information Technology, Artificial Intelligence, or a related field is preferred.
- Relevant hands-on experience building and integrating AI-powered applications.
- Strong problem-solving and analytical skills.
- Ability to understand product requirements and translate them into technical solutions.
- Ability to work independently and collaborate with frontend, backend, QA, and product teams.
- Strong communication, documentation, and debugging skills.

What We Expect From the Candidate The successful candidate should be able to:
- Take an AI feature from concept and technical design through development, testing, and deployment.
- Integrate AI capabilities into existing web and mobile applications.
- Build production-ready AI chatbots, assistants, RAG systems, and automated workflows.
- Choose appropriate AI models, tools, and architectures based on business requirements.
- Troubleshoot AI-related issues and improve performance, reliability, and cost efficiency.
- Maintain secure, scalable, and well-documented AI implementations.

Application Requirements

Interested candidates should submit

- An updated resume.
- Links to relevant AI projects, GitHub repositories, or a technical portfolio.
- Examples of AI-powered applications they have developed or integrated.
- A brief description of their role in those projects and the technologies used.

Portfolio question: Please share a link to your GitHub profile, portfolio, or live AI-powered projects demonstrating your experience in AI integration and application development. Pay: ₹30,000.00 - ₹45,000.00 per month

Benefits

- Paid sick time

Work Location: In person

📌 AI Engineer – AI Integration & Application Development (Panchkula)
🏢 Geek Techs Pro
📍 Panchkula