AI Ml Engineer

Role & Responsibilities

• Design, develop, and deploy AI/ML models for production environments.
• Build and optimize Large Language Model (LLM) based applications using OpenAI, Claude, Gemini, or open-source models.
• Develop RAG (Retrieval-Augmented Generation) pipelines using vector databases such as Pinecone, Weaviate, Milvus, or ChromaDB.
• Fine-tune and evaluate machine learning and deep learning models for domain-specific use cases.
• Work with NLP, Computer Vision, OCR, and Generative AI technologies.
• Design scalable AI microservices and integrate them with web and mobile applications through REST/GraphQL APIs.
• Build intelligent chatbots, AI agents, recommendation systems, and automation workflows.
• Perform data preprocessing, feature engineering, model training, evaluation, and performance optimization.
• Deploy AI models using Docker, Kubernetes, AWS, Azure, or GCP.
• Monitor model performance, reduce latency, and improve inference efficiency.
• Collaborate with product managers, backend developers, and frontend teams to deliver AI-powered features.
• Stay updated with the latest advancements in AI, LLMs, multimodal AI, and open-source frameworks.

Preferred Candidate Profile

• 3-6 years of hands-on experience in AI/ML development.
• Strong proficiency in Python and machine learning libraries such as PyTorch, TensorFlow, Scikit-learn, Hugging Face, LangChain, and LlamaIndex.
• Experience working with OpenAI, Anthropic Claude, Gemini, Llama, Mistral, or similar LLMs.
• Hands-on experience with RAG architecture, embeddings, vector databases, and semantic search.
• Strong knowledge of NLP, Deep Learning, Computer Vision, OCR, and Generative AI.
• Experience in prompt engineering, AI agent development, and workflow automation.
• Familiarity with MLOps, model deployment, Docker, Kubernetes, CI/CD, and cloud platforms (AWS/Azure/GCP).
• Experience working with SQL/NoSQL databases and REST/GraphQL APIs.
• Strong understanding of software engineering best practices, system design, and scalable architecture.
• Excellent analytical, problem-solving, and communication skills.
• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field.