Roles & responsibilities
Here are some of the key responsibilities of AI Architect:
Work on the Implementation and Solution delivery of the AI applications leading the team across onshore/offshore and should be able to cross-collaborate across all the AI streams.
Design end-to-end AI applications, ensuring integration across multiple commercial and open-source tools.
Work closely with business analysts and domain experts to translate business objectives into technical requirements and AI-driven solutions and applications. Partner with product management to design agile project roadmaps, aligning technical strategy. Work along with data engineering teams to ensure smooth data flows, quality, and governance across data sources.
Lead the design and implementations of reference architectures, roadmaps, and best practices for AI applications.
Fast adaptability with the emerging technologies and methodologies, recommending proven innovations.
Identify and define system components such as data ingestion pipelines, model training environments, continuous integration/continuous deployment (CI/CD) frameworks, and monitoring systems.
Utilize containerization (Docker, Kubernetes) and cloud services to streamline the deployment and scaling of AI systems. Implement robust versioning, rollback, and monitoring mechanisms that ensure system stability, reliability, and performance.
Ensure the implementation supports scalability, reliability, maintainability, and security best practices.
Project Management: You will oversee the planning, execution, and delivery of AI and ML applications, ensuring that they are completed within budget and timeline constraints. This includes project management defining project goals, allocating resources, and managing risks.
Oversee the lifecycle of AI application development—from design to development, testing, deployment, and optimization.
Enforce security best practices during each phase of development, with a focus on data privacy, user security, and risk mitigation.
Provide mentorship to engineering teams and foster a culture of continuous learning.
Lead technical knowledge-sharing sessions and workshops to keep teams up-to-date on the latest advances in generative AI and architectural best practices.
Roles & responsibilities
Here are some of the key responsibilities of AI Architect:
Work on the Implementation and Solution delivery of the AI applications leading the team across onshore/offshore and should be able to cross-collaborate across all the AI streams.
Design end-to-end AI applications, ensuring integration across multiple commercial and open-source tools.
Work closely with business analysts and domain experts to translate business objectives into technical requirements and AI-driven solutions and applications. Partner with product management to design agile project roadmaps, aligning technical strategy. Work along with data engineering teams to ensure smooth data flows, quality, and governance across data sources.
Lead the design and implementations of reference architectures, roadmaps, and best practices for AI applications.
Fast adaptability with the emerging technologies and methodologies, recommending proven innovations.
Identify and define system components such as data ingestion pipelines, model training environments, continuous integration/continuous deployment (CI/CD) frameworks, and monitoring systems.
Utilize containerization (Docker, Kubernetes) and cloud services to streamline the deployment and scaling of AI systems. Implement robust versioning, rollback, and monitoring mechanisms that ensure system stability, reliability, and performance.
Ensure the implementation supports scalability, reliability, maintainability, and security best practices.
Project Management: You will oversee the planning, execution, and delivery of AI and ML applications, ensuring that they are completed within budget and timeline constraints. This includes project management defining project goals, allocating resources, and managing risks.
Oversee the lifecycle of AI application development—from design to development, testing, deployment, and optimization.
Enforce security best practices during each phase of development, with a focus on data privacy, user security, and risk mitigation.
Provide mentorship to engineering teams and foster a culture of continuous learning.
Lead technical knowledge-sharing sessions and workshops to keep teams up-to-date on the latest advances in generative AI and architectural best practices.
Mandatory technical & functional skills
Solid experience in enterprise full-stack architecture with a strong product engineering background.
Ability to manage multiple projects and teams in parallel with excellent cross-functional collaboration skills.
Proven expertise in developing or working with AI agents using frameworks such as LangChain, LangGraph, AutoGen, CrewAI, or similar.
Proficiency in Python, C++, and Java, along with deep knowledge of ML libraries and frameworks like TensorFlow, PyTorch, and Keras.
Strong theoretical understanding of deep learning and NLP algorithms, including RNN, CNN, LSTM, and Transformer architectures.
Familiarity with open-source model libraries (e.g., Hugging Face Transformers), OpenAI API integrations, and domain-specific tools. Solid grasp of generative techniques such as GANs, VAEs, diffusion models, and autoregressive models.
Hands-on experience in training and fine-tuning Large Language Models (LLMs) or SLMs using techniques like PEFT (LoRA/QLoRA).
Proven track record in leveraging cloud platforms (AWS, Azure, GCP) for scalable AI solutions, with expertise in large-scale ML deployments and strong knowledge of DevOps/MLOps/LLMOps.
Expertise in designing distributed systems, RESTful APIs, GraphQL integrations, and microservices architecture. Knowledge of event-driven architectures and message brokers (e.g., RabbitMQ, Apache Kafka) for robust inter-system communication.
Demonstrated contributions to open-source projects or published research in relevant domains.
This role is for you if you have the below
Educational qualifications
Bachelors ( BE/BTech) /Master’s degree in Computer Science (MSc/MTech/MS)/ PhD ( CSE, IT, AI, Mathematics, Statistics, Data Science )
Certifications in Cloud technologies (AWS, Azure, GCP) and must have TOGAF certification or any equivalent enterprise experience
Work experience: 14+ Years of Experience
