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.