Role Overview:

We are looking for a Principal Data Scientist / AI-ML Architect to define the technical strategy, architecture, and implementation standards for enterprise AI and machine learning initiatives. The role will drive the development of scalable ML platforms, advanced modeling solutions, production AI systems, and responsible AI practices while influencing technology decisions across teams.

Key Responsibilities:

- Define the AI/ML architecture and technical roadmap aligned with business objectives and enterprise technology strategy.

- Lead the design of end-to-end machine learning systems, covering data ingestion, feature engineering, training, evaluation, deployment, and monitoring.

- Identify high-value AI opportunities and translate complex business problems into scalable data science and machine learning solutions.

- Guide the selection of algorithms, model architectures, frameworks, and deployment strategies.

- Establish standards for experimentation, model validation, reproducibility, performance measurement, and model governance.

- Architect scalable model training and inference systems, including distributed processing and high-throughput serving where required.

- Drive MLOps and LLMOps capabilities, including automated pipelines, model registries, deployment controls, monitoring, and lifecycle management.

- Evaluate emerging AI technologies, including deep learning, generative AI, NLP, and foundation models, based on business requirements.

- Establish strategies for model explainability, bias mitigation, responsible AI, data privacy, and regulatory compliance.

- Guide model optimization for accuracy, latency, throughput, scalability, and infrastructure cost.

- Collaborate with enterprise architects, data engineering leaders, product teams, and business stakeholders to integrate AI capabilities into production systems.

- Lead technical reviews, assess architectural trade-offs, and resolve complex model and production engineering challenges.

- Mentor senior data scientists, ML engineers, and technical leads while building a strong AI engineering culture.

- Drive reusable ML components, shared platforms, and standardized development frameworks across projects.

- Define success metrics and communicate the technical and business impact of AI initiatives to senior stakeholders.

Required Skills:

- Extensive expertise in Python, machine learning, statistical modeling, and advanced analytical methods.

- Deep understanding of classical machine learning, deep learning, optimization, and model evaluation.

- Proven experience designing and deploying production-grade AI/ML systems at enterprise scale.

- Strong knowledge of ML architecture, distributed training, model serving, feature engineering, and scalable inference.

- Expertise in MLOps practices, model lifecycle management, experiment tracking, and automated deployment.

- Experience with cloud-based AI/ML ecosystems across AWS, Azure, or GCP.

- Strong understanding of data architecture, data quality, feature pipelines, and integration with enterprise data platforms.

- Knowledge of model monitoring, drift detection, explainability, and responsible AI governance.

- Experience evaluating and integrating modern AI approaches, including generative AI and LLM-based solutions, where applicable.

- Ability to define technical standards, assess emerging technologies, and guide architectural decisions across teams.

- Demonstrated experience mentoring technical leaders and influencing cross-functional engineering initiatives.

Preferred Qualifications:

- Experience architecting enterprise AI platforms or shared machine learning infrastructure.

- Exposure to LLMOps, retrieval-augmented generation (RAG), model evaluation frameworks, and AI application observability.

- Experience with distributed computing frameworks, feature stores, model registries, and scalable inference infrastructure.

- Familiarity with enterprise AI governance, security, privacy, and compliance requirements.

- Experience developing AI/ML roadmaps and aligning technology investments with measurable business value.

Key Competencies:

- Strategic AI leadership, architecture ownership, advanced problem-solving, technical mentorship, responsible AI decision-making, and the ability to translate enterprise objectives into scalable AI capabilities.