AI Application Architect

You are an experienced AI Application Architect responsible for leading the design and integration of AI/ML capabilities into enterprise applications. Your main objective is to architect intelligent, scalable, and reliable AI solutions that are in line with both business goals and technical strategies. You will work closely with data scientists, ML engineers, and application developers to ensure seamless end-to-end solutions. Additionally, you will be required to select and implement appropriate AI frameworks, APIs, LLMs, and infrastructure tools, as well as drive architecture decisions related to GenAI, NLP, CV, predictive analytics, and agentic AI systems. You will also establish MLOps pipelines for training, testing, and deploying models at scale while ensuring compliance with AI ethics, privacy laws, and data governance policies. Furthermore, you will evaluate emerging technologies, tools, and platforms for enterprise use and act as a technical advisor to leadership on AI opportunities and risks. In terms of required skills, you should have a strong background in AI/ML architecture and solution design, along with hands-on experience in ML frameworks such as TensorFlow, PyTorch, and Scikit-learn. Proficiency in LLMs and generative AI tools like OpenAI, Azure OpenAI, LangChain, and Hugging Face is necessary. A solid programming background in Python (FastAPI, Flask) and familiarity with Java/Node.js are essential. Experience with cloud platforms (AWS/GCP/Azure) and ML toolkits like SageMaker, Azure ML, and Vertex AI is also crucial. Additionally, a good understanding of microservices, REST APIs, GraphQL, and event-driven architecture is required. Knowledge of vector databases such as Pinecone, FAISS, Chroma, or Weaviate, as well as proficiency with CI/CD, Docker, Kubernetes, MLflow, Airflow, or similar tools, is expected. Preferred qualifications include experience with multi-agent systems, LangChain, Autogen, or Agentic AI frameworks, familiarity with data governance, model drift detection, and performance monitoring, and prior experience in industries like BFSI, Retail, Healthcare, or Manufacturing. Education-wise, a Bachelor's or master's degree in computer science, Artificial Intelligence, Data Science, or a related field is required. Certifications in AI/ML, cloud (AWS/GCP/Azure), or MLOps are considered a plus.,