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Sr. Architect

Cognizant -
Chennai, Tamil Nadu

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Job details

Full-time

Qualifications

TensorFlow
Azure
Software deployment
Machine learning
APIs
Scalability
AI
Communication skills
Python

Full job description

Job Summary
This hybrid role for a Senior Architect with 22 to 25 years of experience focuses on designing and delivering scalable AI and machine learning solutions using hands-on AI architect/strategist. Someone who can define AI assurance strategy work with their CoE and business and Translate use cases into execution. The role emphasizes robust architecture operational reliability and responsible AI to support provider domain initiatives with high business impact.

Responsibilities
Design and implement end to end AI architectures that use MLFlow and Python to deliver robust and scalable machine learning solutions for enterprise grade applications
Develop and maintain standardized workflows for experiment tracking and model lifecycle management using MLFlow to improve transparency and reproducibility across projects
Build and optimize high performance application programming interfaces using FastAPI to expose machine learning models as reliable services for internal and external consumers
Create secure and efficient model serving strategies that address latency scalability and observability requirements while aligning with organizational risk and compliance expectations
Design evaluation frameworks using OpenAI playground and Promptflow to systematically test prompts models and configurations for accuracy safety and user experience
Guide cross functional teams in adopting best practices for prompt engineering and experimentation so that AI features provide consistent and explainable outcomes for business users
Develop reusable Python libraries templates and reference implementations that accelerate delivery of AI solutions and reduce technical debt across multiple initiatives
Collaborate closely with product engineering and operations partners to translate complex provider domain needs into clear technical designs and implementation roadmaps
Establish monitoring alerting and observability standards for model serving and FastAPI services to ensure high availability and rapid issue detection in production environments
Drive continuous improvement in data quality feature engineering and deployment pipelines by applying systematic root cause analysis and evidence based decision making
Align architectural decisions with organizational goals by documenting tradeoffs constraints and future evolution paths so that stakeholders can plan investments responsibly
Mentor engineering teams on advanced topics such as MLFlow usage model governance responsible AI and secure coding so that capabilities scale sustainably over time
Partner with security and compliance specialists to design architectures that safeguard data privacy reduce bias risk and meet regulatory expectations in the provider focused solutions

Qualifications
Display deep experience in Python MLFlow and end to end machine learning solution design gained through long term work in complex enterprise environments
Show proven expertise in building and operating APIs with FastAPI including performance optimization testing automation and observability practices
Demonstrate hands on experience with model serving frameworks and deployment patterns covering containerization scalability patterns and rollback strategies
Exhibit practical familiarity with OpenAI playground and Promptflow for designing evaluating and managing prompt based and generative AI solutions
Illustrate understanding of provider domain processes terminology and workflows so that AI solutions align well with practical healthcare or related use cases
Show experience working in hybrid work models and collaborating effectively with distributed teams using modern communication and planning tools
Highlight strong communication skills for explaining complex AI architecture choices to both technical and nontechnical stakeholders in clear and concise ways
Display commitment to responsible AI including fairness transparency and governance practices that align with organizational standards and societal expectations

Certifications Required
Preferred certifications include Microsoft Azure AI Engineer or Architect and TensorFlow Developer or equivalent AI and cloud credentials

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