Solution Architect - Data & AI

As a Hands-on Architect in ProArch s Data & AI practice, you will play a critical role in building innovative solutions, supporting presales, and enabling delivery teams. Your expertise and consultative approach will help navigate vagueness and create impactful outcomes for clients.

Key Responsibilities:

Continuous Innovation:

Idea Generation and Evaluation: Build and maintain a list of ideas, evaluated using the Idea Approval Committee.

o Assess feasibility using criteria such as transferability across domains, strategic fit, market potential, and revenue impact.

o Contribute to building MVPs and launching them in the market.

Sample Innovation Areas:

o IoT Testing

o Blockchain in Testing

o AI for Safety

o Generative AI Applications: BDD Testing, Automated Test Authoring

o Modernizing applications using Computer Vision and Generative AI

Presales:

• Support the Sales team in the pre-sales process
• Provide support in creating slide decks to provide technical information to prospective clients
• Own the demos and technical aspects of the presentations to prospective clients.
• Do a proper hand over to the delivery team and provide support as needed.

Delivery Problem-Solving:

Provide technical expertise to resolve complex challenges faced by delivery teams.

Education for Sales and Marketing Teams:

Conduct workshops and knowledge-sharing sessions to enable sales and marketing teams to present new products and solutions effectively.

Sales Insights Sharing:

Share detailed summaries from sales calls with solutioning teams to offer insights into customer needs.

Technical Expertise:

Design and deploy end-to-end solutions in Data Engineering, Machine Learning (ML), AI, and full-stack development.

Build data pipelines using tools such as Apache Spark, Databricks, Kafka, or Snowflake.

Expertise in AI/ML frameworks: TensorFlow, PyTorch, Scikit-learn, Hugging Face, or LangChain.

Experience in building and deploying Generative AI applications, including LLM fine-tuning and integration.

Advanced knowledge in NLP, Computer Vision, and Recommendation Systems.

Full-stack expertise:

Frontend: React, Angular, or Vue. js.

Backend: Node. js, Python (Flask/Django), Java (Spring Boot), or . NET.

Databases: SQL Server, PostgreSQL, MongoDB, or Cosmos DB.

Proficiency in cloud platforms (Azure, AWS, GCP):

AI services (e. g. , Azure Cognitive Services, AWS SageMaker, GCP Vertex AI).

Serverless architectures and microservices.

Cloud-native data services (e. g. , Azure Synapse, AWS Redshift, GCP BigQuery).

Experience in developing APIs (REST/GraphQL) and integrating with third-party systems.

Strong understanding of DevOps and CI/CD pipelines: Jenkins, GitHub Actions, Azure DevOps, Docker, Kubernetes.