Job Description:

Roles and Responsibilities:

- Define and drive the end-to-end AI/ML architecture, technology strategy, and roadmap aligned with business objectives.

- Design scalable, secure, and production-ready AI/ML solutions across cloud platforms, including AWS and Azure.

- Architect end-to-end Machine Learning and Deep Learning pipelines, covering data ingestion, preprocessing, model training, evaluation, deployment, and monitoring.

- Design and implement Generative AI solutions using LLMs, RAG pipelines, fine-tuning, prompt engineering, and model evaluation frameworks.

- Establish MLOps best practices for model versioning, experiment tracking, CI/CD, deployment automation, monitoring, and continuous improvement.

- Evaluate and select appropriate AI/ML frameworks, cloud services, tools, and platforms based on business and technical requirements.

- Define architectural standards for APIs, microservices, data platforms, containerization, Kubernetes, and cloud-native AI applications.

- Ensure AI/ML solutions meet enterprise requirements for security, scalability, reliability, performance, cost optimization, and data privacy.

- Lead and mentor AI/ML engineers, data scientists, and technical leads, providing architectural guidance and technical oversight.

- Collaborate with business stakeholders, product teams, data engineers, and enterprise architects to translate business requirements into effective AI solutions.

- Establish model governance, responsible AI practices, explainability, bias detection, and compliance standards.

- Drive proof of concepts, technical assessments, architecture reviews, and the transition of AI/ML solutions from experimentation to production.

- Work with global clients to understand business challenges, present architectural recommendations, and deliver enterprise-grade AI/ML solutions.

Skillset:

- Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, or a related field.

- 12+ years of experience in AI/ML, with at least 4 years in a leadership or architecture role such as AI/ML Lead, Principal Data Scientist, or ML Architect.

- Proven experience designing, building, and deploying production-grade AI/ML solutions in cloud environments such as AWS and Azure.

- Strong programming skills in Python and SQL, with experience in REST APIs, Git, Docker, and Kubernetes.

- Deep understanding of Machine Learning techniques, including supervised and unsupervised learning, Deep Learning, NLP, and Computer Vision.

- Strong experience with Generative AI, Large Language Models (LLMs), fine-tuning, prompt engineering, Retrieval-Augmented Generation (RAG), and model evaluation frameworks.

- Experience with MLOps tools and platforms such as MLflow, Kubeflow, Amazon SageMaker, and Databricks.

- Strong understanding of CI/CD pipelines, model deployment, monitoring, scalability, and performance optimization using tools such as Prometheus and Datadog.

- Experience designing secure, scalable, and highly available AI/ML architectures for enterprise applications.

- Experience working with global clients across BFS, Healthcare, Insurance, Publishing, or Enterprise Software domains.

- Excellent technical leadership, architectural decision-making, stakeholder management, and communication skills.