Hiring for our client, a fast-growing enterprise technology company focused on accelerating Data, AI, and Digital Innovation initiatives for global organizations. Founded by industry veterans with a strong track record of building successful technology businesses, the company offers a modern platform that helps enterprises rapidly design, build, deploy, and govern AI-powered solutions at scale. Their solutions span Data Engineering, Machine Learning, Generative AI, Agentic AI, Cloud Platforms, and Digital Product Innovation, enabling organizations to move from concept to production significantly faster.

About the Role:

We are seeking an experienced AI/ML Architect to lead the design and implementation of enterprise-scale AI, Machine Learning, and Generative AI solutions. This role will drive end-to-end AI architecture, covering data engineering, model development, MLOps, deployment frameworks, AI governance, and production-scale AI systems.

The ideal candidate will possess deep expertise across traditional machine learning, large language models (LLMs), retrieval-augmented generation (RAG), AI agents, cloud-native ML platforms, and modern MLOps practices. You will collaborate closely with engineering, product, and business stakeholders to architect scalable AI platforms and deliver innovative AI-powered business solutions.

Skills & Experience:

- 14+ years of overall technology experience with significant experience in AI/ML architecture

- Strong expertise in Machine Learning algorithms and statistical modeling

- Hands-on experience in Data Engineering, ETL/ELT pipelines, and large-scale data processing

- Strong knowledge of data preprocessing, feature engineering, data validation, and model optimization

- Experience designing and implementing MLOps frameworks including MLFlow, Kubeflow, DVC, CI/CD pipelines, model monitoring, and model lifecycle management

- Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, Scikit-Learn, Keras, XGBoost, and LightGBM

- Experience with Databricks, Snowflake, and cloud-native data platforms

- Hands-on expertise with AWS SageMaker, Azure Machine Learning, Google AI Platform, AWS Bedrock, and related AI services

- Strong understanding of LLMs including OpenAI, Gemini, BERT, LLaMA, and enterprise GenAI solutions

- Experience building RAG pipelines, Vector Database solutions, and AI Agent architectures

- Knowledge of AI governance, security, compliance, and responsible AI practices

- Experience working with Kubernetes, Docker, Apache Airflow, and cloud-native deployment architectures

- Exposure to Salesforce, SAP, and enterprise system integrations is preferred

- Strong stakeholder management, technical leadership, and solution architecture skills

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