Sr. AI Engineer - R01572484

1. Lead the design and architecture of end-to-end enterprise AI platforms, ensuring scalability, reliability, and operational readiness
2. Define and implement frameworks for model lifecycle management, MLOps/LLMOps, AI observability, and operationalization
3. Establish Responsible AI principles, governance frameworks, and technical guardrails to ensure compliance and ethical AI deployment
4. Drive platform-level technical decision-making to enable consistent and scalable enterprise AI adoption
5. Provide architectural leadership across business, data, technology, and platform teams to translate AI strategy into production-ready solutions
6. Evaluate emerging AI technologies and assess their applicability to enterprise AI platforms
7. Guide teams in selecting appropriate models, platforms, frameworks, and architectural patterns based on business and technical requirements
8. Collaborate with senior stakeholders to define the enterprise AI roadmap and platform strategy

• Problem Formulation (Business problem to Data Science Problem), OKR Validation against statistical measures, Data Wrangling, Data Storytelling & Insight Generation, Problem Solving, Excel VBA, Data Curiosity, Technical Decision Making (How many iterations to go for vs when to stop iterating), Communication & Articulation: Vocal & Written, Business Acumen (Consume new domains quickly to learn through data), Design Thinking, Data Literacy, Experience Range: With at least 7 years of experience in data science and AI, including significant leadership in designing and operationalizing enterprise-scale AI platforms, 1. Expertise in enterprise AI platform architecture and operationalization 2. Advanced proficiency in MLOps/LLMOps frameworks and tools 3. Deep understanding of Responsible AI principles, governance, and compliance 4. Hands-on experience with cloud-based AI platforms (AWS, Azure, Google Cloud Platform) 5. Technical leadership in model lifecycle management and AI observability 6. Strong knowledge of GenAI architectures, LLMs, and AI orchestration 7. Experience with scalable, secure, and production-ready AI solutions 8. Proficiency in Python and relevant AI/ML libraries (e.g., TensorFlow, PyTorch, scikit-learn)

Preferred Skills:

1. Experience establishing enterprise AI Centers of Excellence or platform teams
2. Knowledge of vector databases, RAG, and AI agents
3. Experience developing reusable AI architecture patterns and accelerators
4. Familiarity with business intelligence and reporting automation
5. Experience mentoring or leading cross-functional AI teams, 1. Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, or a closely related discipline
6. Certification in Data Science or Advanced Analytics from a recognized institution (e.g., Microsoft Certified: Azure Data Scientist Associate, IBM Data Science Professional Certificate)
7. Certification in cloud architecture or AI platform engineering (e.g., AWS Certified Solutions Architect, Google Professional Machine Learning Engineer)

Brillio is one of the fastest growing digital technology service providers and a partner of choice for many Fortune 1000 companies seeking to turn disruption into a competitive advantage through innovative digital adoption. Brillio, renowned for its world-class professionals, referred to as “Brillians”, distinguishes itself through their capacity to seamlessly integrate cutting-edge digital and design thinking skills with an unwavering dedication to client satisfaction.

Brillio takes pride in its status as an employer of choice, consistently attracting the most exceptional and talented individuals due to its unwavering emphasis on contemporary, groundbreaking technologies, and exclusive digital projects. Brillio’s relentless commitment to providing an exceptional experience to its Brillians and nurturing their full potential consistently garners them the Great Place to Work certification year after year.