AIML Architect

Role & responsibilities

• Define and lead the architecture of scalable, secure, and high-performance AI/ML solutions aligned with business objectives.
• Design end-to-end machine learning systems, including data ingestion, preprocessing, feature engineering, model training, evaluation, deployment, and monitoring.
• Develop and implement AI solutions using machine learning, deep learning, natural language processing (NLP), and computer vision technologies.
• Architect generative AI applications using large language models (LLMs), retrieval-augmented generation (RAG), vector databases, and AI agent frameworks.
• Evaluate and select appropriate AI/ML algorithms, frameworks, models, and cloud services based on business requirements and technical feasibility.
• Establish MLOps and LLMOps practices to automate model development, deployment, versioning, monitoring, and lifecycle management.
• Design and implement cloud-native AI/ML solutions using AWS, Microsoft Azure, or Google Cloud Platform.
• Ensure AI solutions meet enterprise requirements for scalability, reliability, performance, security, privacy, and responsible AI practices.
• Optimize model accuracy, inference latency, resource utilization, and infrastructure costs.
• Collaborate with data scientists, data engineers, software developers, product managers, and business stakeholders to translate business problems into AI-driven solutions.
• Establish architectural standards, reusable components, technical guidelines, and best practices for AI/ML development.
• Lead technical reviews, conduct proof-of-concept initiatives, assess emerging AI technologies, and recommend suitable solutions.
• Mentor AI/ML engineers and guide cross-functional teams in delivering production-ready AI applications.
• Monitor model performance, data drift, bias, and system reliability to ensure continuous improvement.

Preferred candidate profile

• Bachelors degree in Computer Science, Artificial Intelligence, Information Technology, Engineering, Mathematics, or a related discipline.
• Master’s degree in AI/ML, Data Science, Computer Science, or a related field is preferred.
• 5 to 10 years of experience in software engineering, data science, machine learning engineering, or AI solution development, with demonstrated architecture and technical leadership experience.
• Strong programming skills in Python and proficiency with SQL, along with hands-on experience in TensorFlow, PyTorch, or Scikit-learn.
• Proven expertise in machine learning, deep learning, model evaluation, feature engineering, and production model deployment.
• Experience with generative AI, LLMs, prompt engineering, RAG, embeddings, vector databases, and frameworks such as LangChain or LangGraph.
• Strong knowledge of MLOps/LLMOps tools and practices, including MLflow, model monitoring, CI/CD, automated testing, and model lifecycle management.
• Hands-on experience with cloud AI/ML services across AWS, Microsoft Azure, or Google Cloud Platform.
• Familiarity with Docker, Kubernetes, REST APIs, microservices, distributed systems, and scalable solution architecture.
• Understanding of AI governance, responsible AI, model explainability, data privacy, security, and regulatory compliance.
• Demonstrated ability to design end-to-end AI solutions, evaluate architectural trade-offs, and deliver measurable business outcomes.
• Strong problem-solving, communication, stakeholder management, mentoring, and technical leadership skills.
• Relevant cloud, AI/ML, or architecture certifications are an advantage.
• Experience deploying enterprise-scale AI applications and optimizing models for performance, reliability, and cost is highly desirable.