Senior AI Engineer

Job Summary We are seeking a highly skilled Senior AI Engineer to design, develop, and deploy scalable Artificial Intelligence and Machine Learning solutions. The ideal candidate should have strong experience in machine learning, deep learning, Generative AI, Large Language Models (LLMs), MLOps, and cloud-based AI platforms. This role requires close collaboration with data scientists, software engineers, product teams, and business stakeholders to deliver innovative AI-driven solutions.

Key Responsibilities Design, develop, and deploy AI/ML models for business applications. Build and optimize machine learning and deep learning pipelines. Develop and implement Generative AI and LLM-based solutions using frameworks such as LangChain, LlamaIndex, and Hugging Face.

Fine-tune, evaluate, and deploy Large Language Models (LLMs). Design and implement Retrieval-Augmented Generation (RAG) architectures. Develop AI-powered chatbots, virtual assistants, and intelligent automation solutions.

Build scalable APIs and microservices for AI model integration. Implement MLOps practices for model deployment, monitoring, versioning, and maintenance. Collaborate with cross-functional teams to understand business requirements and translate them into AI solutions.

Optimize model performance, scalability, and inference efficiency. Stay current with emerging AI technologies, research, and industry trends. Mentor junior engineers and contribute to AI best practices and architecture decisions.

Required Skills 5–10 years of experience in AI/ML engineering and software development. Strong proficiency in Python and AI/ML libraries such as: TensorFlow PyTorch Scikit-learn Pandas NumPy Experience with Generative AI and Large Language Models (LLMs). Hands-on experience with: OpenAI APIs Hugging Face LangChain LlamaIndex Vector Databases (Pinecone, Weaviate, ChromaDB, FAISS) Experience building RAG-based applications.

Knowledge of prompt engineering and model fine-tuning techniques. Strong understanding of NLP, Deep Learning, and Machine Learning algorithms. Experience with REST APIs and microservices architecture.

Familiarity with Docker, Kubernetes, and CI/CD pipelines. Experience with MLOps tools and frameworks. Cloud & Data Technologies AWS (SageMaker, Bedrock, Lambda, S3) Microsoft Azure (Azure OpenAI, Azure ML) Google Cloud Platform (Vertex AI) SQL and NoSQL databases Data pipelines and ETL processes Preferred Qualifications Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field.

Certifications in AI/ML, Cloud Platforms, or Data Engineering are preferred. Experience with multi-agent AI systems and autonomous workflows is a plus. Exposure to Responsible AI, AI Governance, and Model Security practices.