Senior AI Engineer

Job Responsibilities:Design, develop, and optimize AI/ML solutions for real business use cases.Build and evaluate machine learning, deep learning, and generative AI models.Analyze and preprocess structured and unstructured data to support model development and solution quality.Validate model performance, reliability, scalability, and production readiness.Ensure AI solutions align with enterprise standards for maintainability, quality, and governance.Design and implement LLM-based and Generative AI solutions, including RAG pipelines, prompt engineering, retrieval optimization, and evaluation.Work with embeddings, vector search, reranking, and enterprise knowledge integration patterns.Support use cases such as document intelligence, AI assistants, chatbots, search augmentation, and decision-support solutions.Own AI solution delivery from requirement analysis through deployment, integration, and handover.Collaborate with software, data, and platform teams to integrate AI capabilities into enterprise platforms and APIs.Support monitoring, optimization, and post-go-live improvement of AI services.Apply MLOps / LLMOps practices for reproducibility, deployment automation, monitoring, and lifecycle management.Support technical proposal development, bid responses, and client-facing AI solution discussions when required.Contribute to technical presentations, solution write-ups, and AI capability positioning.Provide technical insight during opportunity shaping and feasibility assessments.Required Qualifications:Bachelor’s in Computer Science, AI, Data Science, Computer Engineering, or related field.From 4–6 years of hands-on experience delivering end-to-end AI/ML solutions in real business environments (not just PoCs/research).Strong Python skills; hands-on with PyTorch, TensorFlow, and Scikit-learn.Experience in data preprocessing, feature engineering, model evaluation, and optimization.Practical experience with Generative AI, LLMs, RAG, embeddings, and vector databases.Familiar with prompt engineering, LLM evaluation, guardrails, and inference optimization.Experience with MLOps/LLMOps (deployment, monitoring, versioning, lifecycle).Cloud experience: Azure AI / Azure OpenAI preferred; AWS/GCP a plus.Experience with APIs, microservices, enterprise integration, and Docker/containers.Experience in domains like NLP, document intelligence, CV, forecasting, recommendations, or conversational AI.Strong analytical, communication, and cross-functional collaboration skills; leadership/mentoring mindset.Proven delivery of deployed AI/ML solutions.Provide project examples with: domain/client, AI approach, role, and deployment environment.Show involvement in deployment, handover, and post-go-live support/optimization.