Zycus looking for an experienced Senior AI Engineer to design, develop, and deploy scalable AI-powered solutions. The ideal candidate will have strong expertise in Machine Learning, Deep Learning, Generative AI, Large Language Models (LLMs), and AI application development.

The candidate will be responsible for building intelligent applications, integrating AI models into enterprise systems, optimizing model performance, and driving AI innovation across business use cases.

Key Responsibilities

• Design, develop, and deploy AI/ML models for real-world business applications.
• Build and implement Generative AI solutions using LLMs such as GPT, Claude, Gemini, and open-source models.
• Develop LLM-based applications using frameworks such as LangChain, LangGraph, and LlamaIndex.
• Design and implement Retrieval-Augmented Generation (RAG) pipelines.
• Work with vector databases such as Pinecone, Weaviate, Milvus, or FAISS.
• Develop AI agents and multi-agent systems for workflow automation.
• Fine-tune and optimize machine learning and deep learning models.
• Build scalable AI services and REST APIs using Python and FastAPI.
• Implement prompt engineering, model evaluation, guardrails, and responsible AI practices.
• Deploy AI solutions on cloud platforms such as AWS, Azure, or GCP.
• Collaborate with product managers, data scientists, architects, and engineering teams.
• Ensure AI solutions meet requirements for scalability, security, accuracy, and performance.
• Mentor junior engineers and contribute to technical architecture and engineering best practices.

Required Technical Skills

Programming & Frameworks

• Strong Python programming skills.
• Experience with FastAPI, Flask, or Django.
• Knowledge of REST APIs, microservices, and distributed systems.
• Experience with Git, Docker, and Kubernetes.

Artificial Intelligence & Machine Learning

• Strong understanding of Machine Learning and Deep Learning.
• Experience with TensorFlow, PyTorch, or Scikit-learn.
• Knowledge of NLP, embeddings, transformers, and neural networks.
• Experience with model training, fine-tuning, and evaluation.

Generative AI & LLM

• Hands-on experience with OpenAI, Azure OpenAI, Anthropic, or Google Gemini APIs.
• Experience with LangChain, LangGraph, or LlamaIndex.
• Strong understanding of RAG architecture and implementation.
• Experience with vector databases and semantic search.
• Knowledge of prompt engineering and LLM evaluation.
• Experience building AI agents and agentic workflows.

Cloud & MLOps

• Experience with AWS, Azure, or Google Cloud.
• Knowledge of CI/CD pipelines and model deployment.
• Experience with MLflow, Kubeflow, or equivalent MLOps tools.
• Understanding of model monitoring, observability, and production optimization.

Database Technologies

• Experience with SQL and NoSQL databases.
• Knowledge of PostgreSQL, MongoDB, Redis, and vector databases.

Preferred Qualifications

• Experience building enterprise-grade Generative AI applications.
• Experience with multi-agent architectures and autonomous workflows.
• Knowledge of fine-tuning techniques such as LoRA and QLoRA.
• Experience with multimodal AI applications involving text, images, audio, or video.
• Knowledge of AI security, privacy, hallucination mitigation, and responsible AI.
• Experience optimizing LLM inference costs and latency.

Education

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
• Relevant AI/ML certifications are an added advantage.

Key Competencies

• Strong analytical and problem-solving skills.
• Excellent software engineering and system design capabilities.
• Ability to independently own AI projects from design to production.
• Strong communication and collaboration skills.
• Ability to mentor team members and drive technical innovation.

Success Criteria

• Successful development and deployment of production-ready AI solutions.
• Improved business efficiency through AI automation.
• High-quality, reliable, and scalable AI applications.
• Effective implementation of GenAI, RAG, and AI agent solutions.
• Optimization of model performance, infrastructure costs, and response latency.