CATT Tech Delivery - AI Solution Architect

Applied AI Solutions Architect:

Job Overview

The Applied AI Solutions Architect is a strategic role responsible for designing, implementing, and managing AI-driven solutions that align with business objectives. This position bridges technical expertise in artificial intelligence (AI) and machine learning (ML) with business strategy, ensuring scalable, ethical, and high-performing AI systems. The AI Solutions Architect collaborates with cross-functional teams to deliver innovative solutions, leveraging generative AI, cloud platforms, and modern architectures like Retrieval-Augmented Generation (RAG).

Responsibilities

• Solution Design: Architect end-to-end AI/ML pipelines, including data ingestion, preprocessing, model training, deployment, and monitoring, ensuring scalability and performance.
• Technology Selection: Evaluate and select appropriate AI frameworks, tools, and cloud services (e.g., AWS SageMaker, Azure AI, Google Cloud AI) based on project requirements.
• Generative AI Implementation: Design solutions using large language models (LLMs) and RAG architectures for applications like content generation, customer engagement, or product design.
• Collaboration: Work with data scientists, engineers, product managers, and executives to translate business needs into technical solutions, acting as a trusted advisor.
• Governance and Ethics: Implement responsible AI practices, addressing bias, security, and compliance in AI systems.
• MLOps and AIOps: Establish CI/CD pipelines, model versioning, and monitoring frameworks to operationalize AI solutions.
• Thought Leadership: Advocate for AI-driven innovation, mentor teams, and communicate technical concepts to non-technical stakeholders.
• Performance Optimization: Ensure AI solutions meet latency, cost, and quality requirements, optimizing for production environments.

Skill Set

• Technical Skills:
  • Proficiency in Python, R, or Julia for AI/ML development.
  • Expertise in ML frameworks like TensorFlow, PyTorch, Hugging Face, or Scikit-learn.
  • Experience with cloud platforms (AWS, Azure, Google Cloud) and AI services like Amazon Bedrock or Azure AI Foundry.
  • Knowledge of MLOps tools (e.g., Kubeflow, MLflow) and CI/CD pipelines.
  • Familiarity with generative AI techniques, including prompt engineering, fine-tuning, and RAG.
  • Understanding of data engineering concepts, including ETL processes and data lakes.
• Soft Skills:
  • Strong communication to bridge technical and business teams.
  • Analytical thinking for evaluating trade-offs and designing optimal solutions.
  • Leadership and mentorship to guide cross-functional teams.
• Domain Knowledge:
  • Experience in industries like healthcare, finance, or technology, with an understanding of relevant use cases (e.g., drug discovery, personalized marketing).

Certifications

• AWS Certified Machine Learning Specialty
• Microsoft Azure AI Engineer Associate
• Google Cloud Professional Machine Learning Engineer
• Coursera or Edureka AI/ML certifications (e.g., DeepLearning.AIs Generative AI Specialization)
• ITIL or TOGAF for enterprise architecture alignment (optional)

Qualifications

• Bachelors or Masters degree in Computer Science, Data Science, or a related field.

• 8+ years of experience in ML engineering, data science, or software architecture, with 3+ years in AI/ML solution design.

• Proven track record of deploying AI solutions in production environments