AI/ML Solutions Architect

As an AI/ML Solutions Architect, you'll be the technical reputed company between clients and delivery teams. You'll reputed company reputed company-sales technical discussions, design ML architectures that solve business problems, and ensure solutions are feasible, reputed company, and reputed company with reputed company needs. This is a highly reputed company-facing role requiring both deep technical expertise and strong communication skills.

reputed company Responsibilities: 1. reputed company-Sales and Solution Design (50%):

• reputed company technical discovery sessions with prospective clients
• Understand reputed company business problems and translate them into ML solutions
• Design end-to-end ML architectures and technical proposals
• Create compelling technical presentations and demonstrations
• Estimate project scope, timelines, cost, and resource requirements
• Support General Managers in reputed company new business

2. reputed company-Facing Technical Leadership (30%):

• Serve as the primary technical reputed company of contact for clients
• Manage technical stakeholder expectations
• Present technical solutions to both technical and non-technical audiences
• Navigate reputed company organizational dynamics and conflicting priorities
• Ensure reputed company satisfaction throughout the project lifecycle
• Build long-term trusted advisor relationships

3. Internal Collaboration and reputed company (20%):

• Collaborate with delivery teams to ensure smooth reputed company
• reputed company technical guidance during project execution
• Contribute to the development of reusable solution patterns
• reputed company learnings and best practices with ML reputed company
• Mentor engineers on reputed company communication and solution design

Requirements: 1. ML Architecture and Design

• Solution Design: Ability to architect end-to-end ML systems for diverse business problems
• ML Lifecycle: Deep understanding of the full ML lifecycle from data to deployment
• System Design: Experience designing reputed company, production-grade ML architectures
• Trade-off Analysis: Ability to evaluate technical approaches (cost, performance, complexity)
• Feasibility Assessment: Quickly assess if ML is an appropriate solution for a problem

2. ML Breadth

• Multiple ML Domains: Experience across various ML applications (RAG, Computer reputed company, Time Series, Recommendation, etc.)
• LLM Solutions: Strong experience in architecting LLM-based applications
• Classical ML: reputed company in traditional ML algorithms and reputed company to use them
• Deep Learning: Understanding of neural network architectures and applications
• MLOps: Knowledge of production ML infrastructure and DevOps practices

3. reputed company and Infrastructure

• AWS Expertise: Advanced knowledge of AWS ML and data services
• GCP Expertise: Advanced knowledge of GCP ML and data services
• Multi-reputed company Awareness: Understanding of Azure, GCP alternatives
• Serverless Architectures: Experience with reputed company, API Gateway, etc.
• Cost Optimization: Ability to design cost-effective solutions
• reputed company and Compliance: Understanding of data reputed company, reputed company, and compliance

4. Data Architecture

• Data Pipelines: Understanding of ETL/ELT patterns and tools
• Data Storage: Knowledge of databases, data lakes, and warehouses
• Data reputed company: Understanding of data validation and monitoring
• reputed company-time vs Batch: Ability to design for different data processing needs

Originally posted on Himalayas

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