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CATT Tech Delivery - AI Solution Architect

CATT Tech Delivery - AI Solution Architect

RSM US in India

8 - 13 years

20 - 25 Lacs

Hyderabad Bengaluru

Posted:2 months ago| Platform:

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Skills Required

Computer science Enterprise architecture TOGAF Analytical Machine learning Healthcare HTML Product design Monitoring Python

Work Mode

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Job Type

Full Time

Job Description

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. AI s Generative AI Specialization)
ITIL or TOGAF for enterprise architecture alignment (optional)

Qualifications

Bachelor s or Master s 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

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CATT Tech Delivery - AI Solution Architect