AI / ML Architect

Job Description

Job Description

A seasoned Enterprise Architect with extensive experience designing and implementing AI-driven solutions across diverse business domains, specializing in developing scalable, robust architectures that leverage machine learning, deep learning, and natural language processing to optimize operations and drive strategic decision-making. Proven ability to collaborate with cross-functional teams, align technology with business objectives, and guide the adoption of cutting-edge AI technologies.

Key Skills :

AI Architecture Design :

Expertise in crafting comprehensive AI architectures encompassing data ingestion, preprocessing, feature engineering, model training, deployment, and monitoring.

Machine Learning Algorithms :

Proficient in applying various ML algorithms (regression, classification, clustering) and deep learning techniques (CNNs, RNNs, Transformers) to solve complex business challenges.

Cloud Computing :

Deep understanding of cloud platforms (AWS, Azure, GCP) and their AI services for efficient scaling and deployment of AI models.

Data Engineering :

Skilled in data pipeline design, data cleansing, transformation, and integration to ensure high-quality data for AI model training.

Enterprise Architecture Frameworks :

Familiarity with TOGAF, Zachman Framework, and other EA frameworks to align AI initiatives with overall business strategy.

Spearheaded the development of a comprehensive AI architecture to streamline customer service operations, resulting in a 25% reduction in response times and improved customer satisfaction.

Designed and implemented AI-powered predictive analytics models to identify high-risk customer churn, enabling proactive retention strategies.

Led the migration of legacy AI systems to a cloud-based platform, optimizing performance and scalability.

Collaborated with cross-functional teams to define business requirements, data strategy, and model evaluation metrics.

Provided technical guidance and mentorship to junior architects on best practices for designing and deploying AI solutions.

Developed and implemented custom AI models for natural language processing (NLP) to analyze customer feedback, extracting key insights for product improvement.

Designed an AI-powered recommendation system to enhance customer engagement and drive sales conversion rates.

Facilitated the integration of AI models into existing enterprise applications, ensuring seamless data flow and operational efficiency.

Conducted feasibility studies and proof-of-concept demonstrations to evaluate the potential of new AI technologies.

Certifications :

AWS Certified Machine Learning Specialty, Microsoft Azure AI Engineer Associate, and Google Cloud Certified Professional Machine Learning Engineer.