AI Platform Engineer

We are looking for a highly skilled AI Platform Engineer with 4-6 years of experience in building AI-driven applications using large language models (LLMs) and modern AI frameworks. The ideal candidate will have hands-on expertise in designing, developing, and deploying GenAI solutions such as chatbots, automation tools, intelligent assistants, and AI-powered workflows that solve real business problems. You will work closely with cross-functional teams to integrate AI capabilities into products and processes, ensuring scalable, secure, and production-ready implementations.
Responsibilities:
Design, develop, and deploy applications powered by Large Language Models (LLMs).
Build and optimise RAG (Retrieval Augmented Generation) pipelines using vector databases.
Develop AI agents and automation workflows using modern agent frameworks.
Fine-tune, evaluate, and optimise models for performance, accuracy, and cost efficiency.
Integrate AI models with enterprise systems through APIs and microservices.
Implement prompt engineering strategies and guardrails for safe AI usage.
Work on multimodal AI applications (text, images, audio) when required.
Deploy AI solutions on cloud platforms, ensuring scalability and reliability.
Collaborate with product managers, data scientists, and engineers to deliver AI features.
Stay updated with the latest advancements in generative AI technologies.
Requirements:
Bachelor's or Master's of Technology in Computer Science Artificial intelligence, data science, engineering or a related field.
Strong programming experience in Python.
Hands-on experience with LLM APIs (OpenAI, Azure OpenAI, Anthropic, Gemini, etc. ).
Experience with LangChain, LlamaIndex, or similar orchestration frameworks.
Knowledge of vector databases (Pinecone, Weaviate, FAISS, Chroma, and Milvus).
Experience in building RAG-based applications.
Understanding of Prompt Engineering and Model Evaluation Techniques.
Experience with REST APIs, FastAPI, or Flask.
Familiarity with Docker, Git, and CI/CD pipelines.
Cloud and Deployment:
Experience with at least one cloud platform: AWS / Azure / GCP.
Knowledge of Kubernetes (good to have).
Knowledge of serverless deployment.
Knowledge of model monitoring tools.
Preferred Skills (Good to Have):
Experience building AI agents / autonomous systems.
Knowledge of MLOps / LLMOps practices.
Experience with speech-to-text / text-to-speech systems.
Frontend integration knowledge (React / Streamlit / Next.js ).
Experience working with enterprise data systems and databases (SQL / NoSQL).
Understanding of AI safety, governance, and responsible AI principles.

Experience
4-7 yrs