
Overview
We are seeking a seasoned and visionary AI/ML Data Scientist to lead the design and implementation of our AI/ML infrastructure, enabling robust, scalable, and innovative solutions. The ideal candidate will combine deep technical expertise in data science, machine learning, and analytics with a strategic mindset to drive the development of cutting- edge AI/ML architectures. This role also requires strong experience in deploying ML solutions into production using MLOps practices and implementing ML fail-safe mechanisms.
Eligibility Criteria
Years of Experience: 8-12 years
Professional Experience: Proven expertise in designing and developing enterprise-wide data science solutions and scalable architectures.
Education: Bachelor's, Master's, or Ph.D. in Computer Science, Data Science, or a related field.
Responsibilities
- Design and implement end-to-end AI/ML and Generative AI solution aligned with business and scalability requirements.
- Evaluate and select data science tools, frameworks, and platforms to build an efficient and cohesive ecosystem.
- Deliver ML and Gen AI solutions within cloud environments (preferably Azure).
- Be hands-on with ML model development and provide the right solutions for complex business problems.
- Stay updated on emerging AI/ML technologies and ensure continuous enhancement of data science capabilities.
- Collaborate closely with data scientists, engineers, and business stakeholders to translate requirements into architectural solutions.
- Foster a collaborative environment that encourages cross-functional innovation.
- Establish monitoring and governance mechanisms to track model performance and address issues proactively.
- Deliver quick POCs and generate insights from diverse and complex datasets.
- Provide technical leadership for end-to-end solution development and offer hands-on guidance to teams.
Mandatory Skills
- Proven experience as a Data Science Architect or senior AI/ML leader, with a track record of delivering enterprise-scale AI/ML/DL/GenAI solutions.
- Strong proficiency in programming languages such as Python or R, along with expertise in ML/DL frameworks.
- Deep knowledge of machine learning, statistical modeling, and applied data science techniques.
- Experience with cloud platforms (Azure, AWS, or GCP) and big data technologies (Hadoop, Spark, etc.).
- Strong understanding of MLOps and ML fail-safe principles.
- Hands-on experience with project management and agile methodologies (e.g., MS Project).
- Strong stakeholder management skills (internal and external).
- Strategic thinker with the ability to align technology with business outcomes.
- Excellent communication, leadership, and decision-making skills.
- Ability to thrive in a dynamic, collaborative, and fast-paced environment.
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