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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.
- Solid 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.

📌 ValueLabs - AI/ML Architect (India)
🏢 VALUELABS SOLUTIONS
📍 India