About The Role

Focus

We are looking for an experienced AI Engineer to design, build, integrate, and deploy AI-powered solutions for real-world products.

The ideal candidate will have strong hands-on experience with Generative AI, Large Language Models (LLMs), AI APIs, AI application architecture, and production AI systems. This role requires an engineer who can take AI capabilities beyond experimentation and turn them into reliable, scalable, and production-ready product features.

You will work across AI solution design, data and model integration, backend services, user-facing applications, deployment, monitoring, and continuous improvement.

Strong software engineering capabilities are essential, as the role involves building the application layers required to deliver AI solutions in production. The candidate should be comfortable working with backend APIs, frontend applications, databases, mobile application integration, cloud environments, and modern development workflows.

Key Responsibilities

- Design and develop production-ready AI solutions based on business and product requirements.
- Build AI-powered features using Generative AI, Large Language Models, machine learning services, and AI APIs.
- Design AI workflows involving prompt engineering, context management, retrieval, tool calling, function calling, and multi-step processing.
- Develop intelligent features such as AI assistants, chatbots, recommendations, summarization, classification, semantic search, content generation, automation, and decision-support workflows.
- Design and implement Retrieval-Augmented Generation (RAG) solutions and integrate relevant enterprise or product data into AI workflows.
- Work with embeddings, vector search, vector databases, and semantic retrieval systems.
- Build and integrate AI agents and tool-based workflows where appropriate.
- Evaluate AI outputs and continuously improve accuracy, reliability, relevance, latency, and cost efficiency.
- Select appropriate models and AI services based on use case, quality, performance, scalability, and cost requirements.
- Integrate AI capabilities into web and mobile applications through APIs and application services.
- Develop backend services and APIs required to support AI features and application functionality.
- Collaborate with frontend and mobile developers to deliver AI-powered user experiences.
- Work with databases, data pipelines, and application data required by AI systems.
- Participate in architecture discussions and make technical decisions related to AI application design.
- Own AI features end-to-end, from requirement analysis and technical design through development, testing, deployment, monitoring, and production support.
- Investigate and resolve production issues involving application behavior, AI responses, integrations, performance, and data.
- Stay current with emerging AI models, frameworks, tools, and development practices and assess their practical value for products.

Required Skills & ExperienceAI Engineering





- Robust hands-on experience with Generative AI and Large Language Model applications.
- Experience working with OpenAI, Google Gemini, Anthropic, or equivalent AI model providers.
- Strong understanding of prompt engineering and structured prompt design.
- Practical experience building RAG-based applications.
- Experience with embeddings, vector databases, semantic search, and retrieval pipelines.
- Experience with AI agents, function calling, tool calling, or agentic workflows.
- Experience with LangChain, LlamaIndex, Semantic Kernel, or similar AI development frameworks is preferred.
- Strong understanding of model context windows, token usage, latency, scalability, and AI cost optimization.
- Ability to evaluate AI outputs and improve response quality, consistency, and reliability.
- Understanding of hallucination mitigation, grounding, validation, and responsible AI practices.
- Experience integrating AI services into production applications through APIs.
- Strong Python programming skills and familiarity with relevant AI/ML libraries and frameworks.
- Understanding of data preparation, preprocessing, and fundamental machine learning concepts.
- Exposure to model evaluation, observability, prompt/version management, and AI quality monitoring is an advantage.

Software Engineering & Application Development The AI Engineer should also have robust software engineering skills to build and integrate complete AI-powered applications.

Backend

- Strong experience with Python and frameworks such as FastAPI, Django, or equivalent.
- Experience with Node.js, Java, or another backend technology is an advantage.
- Experience developing REST APIs and integrating third-party services.
- Understanding of authentication, authorization, application security, and scalable backend architecture.
- Ability to design backend services capable of supporting AI workloads.

Frontend

- Working experience with React.js, Next.js, or similar frontend technologies.
- Robust understanding of JavaScript/TypeScript and modern web development practices.
- Ability to integrate AI APIs and backend services into user-facing applications.
- Understanding of responsive, intuitive, and user-friendly application interfaces.

Database & Data

- Experience with PostgreSQL, MySQL, MongoDB, or equivalent databases.
- Understanding of database design, queries, indexing, and performance optimization.
- Experience with Redis or similar caching technologies is preferred.
- Experience working with vector databases and AI data stores.

Mobile Application & Product Lifecycle

- Good understanding of the complete application development lifecycle,



from requirements through production.
- Understanding of mobile application development and integration is required.
- Experience with Flutter, React Native, Android, or iOS is an advantage.
- Ability to work effectively with mobile development teams to integrate and deliver AI-powered application features.
- Understanding of application builds, releases, versioning, APIs, notifications, monitoring, and production troubleshooting.
- Ability to understand product requirements and translate them into practical AI and application solutions.

Cloud, Deployment & Production

- Working knowledge of AWS, Azure, or Google Cloud.
- Understanding of deploying AI and application services in cloud environments.
- Experience with Docker and Linux environments.
- Understanding of CI/CD pipelines and automated deployment.
- Experience with Git and collaborative development workflows.
- Familiarity with application monitoring, logging, performance analysis, and production troubleshooting.
- Understanding of scalable, secure, and reliable production architecture.

What We're Looking For

- 4+ years of professional software engineering experience with strong hands-on AI development experience.
- Demonstrated experience building AI-powered applications or production AI features.
- Strong understanding of Generative AI, LLMs, RAG, embeddings, vector search, and AI APIs.
- Ability to combine AI engineering with practical software development to deliver complete product features.
- Experience taking AI features from concept and experimentation through production.
- Strong analytical, problem-solving, debugging, and system-design skills.
- Ability to independently investigate technical problems and develop practical solutions.
- Strong communication and collaboration skills.
- Ability to work effectively with Product, Design, QA, Backend, Frontend, Mobile, and DevOps teams.
- Curiosity and willingness to continuously learn and experiment with rapidly evolving AI technologies.

Good to Have

- Experience working in a SaaS or product-based environment.
- Experience building AI-first products or intelligent consumer and enterprise applications.
- Experience taking products from MVP to production.
- Experience with real-time applications, WebSockets, WebRTC, or event-driven systems.
- Experience with AI evaluation frameworks, model observability, or AI quality monitoring.
- Experience with AWS AI, Azure AI, Google Cloud AI, or equivalent AI services.
- Experience working in an Agile/Scrum environment.

Role Expectations The successful candidate will be expected to think beyond individual AI models or APIs and understand how AI fits into a complete production application. The role requires strong ownership, practical engineering judgment, and the ability to move from problem definition → AI solution design → development → integration → testing → deployment → monitoring → continuous improvement. Skills: application,ai ml,models

📌 AI ENGINEER (Hyderabad)
🏢 Photonx technologies
📍 Hyderabad