AI Platform Engineer

AI Platform Engineer (Product Engineering & Internal Innovation)

Location: 8-13-95/77, Opposite National Police Academy Main Gate, Sivarampally, Hyderabad, Telangana 500052

Experience: 6+ Years

Employment Type: Full-Time

Timing – 9:30 AM KSA time

Department: Technology & Innovation

Prismberry Technology

Prismberry Technologies Pvt. Ltd. is an AI-first IT consulting and digital engineering company specializing in Generative AI, Cloud, Data Engineering, Product Development, SAP, DevOps, Quality Engineering, and IT Staff Augmentation. We partner with global enterprises and GCCs to deliver innovative, scalable, and business-driven technology solutions across India, the USA, and the Middle East.

Position Summary

The AI Platform Engineer will play a key role in designing, developing, and maintaining TechVisions’ internal AI platform and reusable AI components. This role is focused on engineering and platform development rather than customer consulting.

The successful candidate will work closely with Cloud, Security, DevOps, Product Engineering, and Pre-Sales teams to develop scalable AI services that support both customer projects and future AI products.

Key ResponsibilitiesAI Platform Development

• Design and develop enterprise AI platform services
• Build reusable AI APIs and shared platform components
• Develop AI orchestration services and workflow engines
• Build Model Gateway services for multiple LLM providers
• Develop Prompt Management and Prompt Library services
• Build Retrieval-Augmented Generation (RAG) platforms
• Implement AI Observability and Monitoring capabilities

AI Engineering

• Develop high-performance Python applications using FastAPI
• Build Agent-based and Multi-Agent AI systems
• Design AI pipelines and workflow orchestration services
• Develop background processing and asynchronous services
• Build secure REST APIs for enterprise AI applications

AI Model Integration & Deployment

Integrate and deploy AI models and services including:

• OpenAI
• Claude
• Gemini
• Groq
• Ollama
• vLLM
• Open-source Large Language Models (LLMs)

AI Infrastructure

• Deploy and manage AI services using Docker and Kubernetes
• Configure GPU-enabled environments
• Maintain model serving infrastructure
• Implement API Gateway integrations
• Optimize caching and performance for AI workloads

Software Quality & Engineering Excellence

• Write clean, maintainable, and well-documented code
• Develop unit, integration, and API tests
• Participate in code reviews
• Perform performance tuning and optimization
• Support secure software development practices

Required QualificationsEducation

• Bachelor’s degree in Computer Science, Software Engineering, Artificial Intelligence, Information Technology, or a related field.

Experience

• 5-8 years of professional software engineering experience
• Experience developing production-grade AI applications
• Strong Python development experience
• Experience building RESTful APIs
• Experience working in Agile development environments

Required Technical SkillsProgramming & Backend

• Python
• FastAPI
• Async Programming
• REST APIs
• Git

Databases

• PostgreSQL
• Redis

Containerization & Infrastructure

• Docker
• Kubernetes

AI & Machine Learning

Experience with one or more of the following:

• LangChain
• LangGraph
• Model Context Protocol (MCP)
• OpenAI SDK
• Prompt Engineering
• Retrieval-Augmented Generation (RAG)
• Vector Search
• Embeddings
• AI Evaluation Frameworks
• Run:AI
• Ray Serve (Preferred)

AI Infrastructure

Exposure to:

• Ollama
• vLLM
• NVIDIA GPU environments
• CUDA (Basic Knowledge)
• TensorRT (Preferred)

Preferred Qualifications

Experience or knowledge in:

• Cloud Platforms (OCI, Azure, AWS, or Google Cloud)
• Enterprise Architecture
• Enterprise Security
• Identity and Access Management

Soft Skills

• Strong analytical and problem-solving abilities
• Passion for AI and emerging technologies
• Ability to work collaboratively in cross-functional teams
• Excellent communication and documentation skills
• Self-motivated with a continuous learning mindset
• Strong attention to quality and engineering best practices

Key Performance Indicators (KPIs)

Success in this role will be measured by:

• Delivery of production-ready AI services
• Platform stability and reliability
• Code quality and maintainability
• Deployment frequency and release quality
• Development of reusable …