ML Solutions Architect
As an 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.
n- 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
- reputed company 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
- 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, 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
- 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
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