AI/ML Engineer

About the Role

We are seeking an accomplished Senior AI/ML Engineer to spearhead the creation, deployment, and refinement of scalable artificial intelligence systems. The role demands profound expertise in Generative AI, Large Language Models (LLMs), machine learning, data science, and MLOps. The successful candidate will lead innovation efforts, build production-ready AI applications, mentor less experienced engineers, and collaborate closely with cross-functional teams to deliver impactful AI-driven solutions.

Key Responsibilities

• Design and develop scalable architectures and solutions for AI/ML applications.
• Lead projects involving Generative AI, LLMs, chatbots, and intelligent automation.
• Create and enhance Retrieval-Augmented Generation (RAG) pipelines.
• Deploy, monitor, and maintain machine learning models in live production settings.
• Manage MLOps workflows including CI/CD, versioning, monitoring, and automation of models.
• Integrate AI models seamlessly with enterprise applications and APIs.
• Optimize AI model performance focusing on scalability, reliability, and cost efficiency.
• Provide mentorship and conduct technical assessments for junior team members.
• Collaborate with product, engineering, and data teams to plan and implement AI strategies.
• Keep abreast of the latest developments in AI, Generative AI, LLMs, and MLOps technologies.

Required Skills

• Extensive knowledge of machine learning, deep learning, and data science disciplines.
• Proven experience with Generative AI technologies and Large Language Models.
• Skillful in prompt engineering, fine-tuning models, and performance evaluation.
• Strong background in natural language processing, embeddings, vector search methods, and semantic data retrieval.
• Proficiency in Python programming.
• Familiar with frameworks such as TensorFlow, PyTorch, Scikit-learn, LangChain, LlamaIndex, or counterparts.
• Hands-on experience with AI models from OpenAI, Anthropic, Gemini, Claude, Llama, Mistral, or similar.
• Expertise creating production-level Retrieval-Augmented Generation (RAG) systems.
• Experience with vector databases like Pinecone, Weaviate, ChromaDB, Milvus, or FAISS.
• Knowledge of embedding models and advanced retrieval techniques.
• Solid grounding in MLOps including model lifecycle management, CI/CD pipelines, Docker, Kubernetes, and production deployment.
• Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform, including designing scalable cloud-native architectures.
• Understanding of REST APIs, microservices, and distributed systems architectures.