JOB SUMMARY
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).
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Location: Hyderabad (preferred) but open to other locations
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Shift timing: 2 PM – 11 PM IST (no exceptions)
ESSENTIAL DUTIES
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Solution Design: Architect end-to-end AI/ML pipelines, including data ingestion, preprocessing, model training, deployment, and monitoring, ensuring scalability and performance.
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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.
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Generative AI Implementation: Design solutions using large language models (LLMs) and RAG architecture for applications like content generation, customer engagement, or product design.
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Collaboration: Work with data scientists, engineers, product managers, and executives to translate business needs into technical solutions, acting as a trusted advisor.
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Governance and Ethics: Implement responsible AI practices, addressing bias, security, and compliance in AI systems.
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MLOps and AIOps: Establish CI/CD pipelines, model versioning, and monitoring frameworks to operationalize AI solutions.
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Thought Leadership: Advocate for AI-driven innovation, mentor teams, and communicate technical concepts to non-technical stakeholders.
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Performance Optimization: Ensure AI solutions meet latency, cost, and quality requirements, optimizing production environments.
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Monitor and tune workloads within the Microsoft Fabric platform to ensure cost-effective and efficient operations
TECHNICAL SKILLS
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AI Techniques: Expertise with the concepts of generative AI, prompt engineering, fine-tuning, and RAG.
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Azure Ecosystem: Expertise in Azure Machine Learning, Azure OpenAI Service (GPT, Codex, DALL·E), Azure AI Search (RAG pattern), Azure Cognitive Services, and Azure DevOps.
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AI Frameworks: Familiarity with Microsoft Semantic Kernel, Autogen C#, PyTorch, Scikit-learn, ONNX, and Hugging Face Transformers.
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Programming: Skilled in Python and C# for AI/ML development, with expertise in vector search using Azure Cosmos DB, prompt engineering, and content safety filters.
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Data Tools: Familiarity with SQL Server and Cosmos DB.
ADDITIONAL ASSETS
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DevOps: Knowledge of version control (Git) and CI/CD (Azure DevOps, Docker, Kubernetes via Azure Kubernetes Service).
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Data Engineering: Understanding of ETL processes and data lakes.
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Microsoft Purview: Understanding of Microsoft Purview for data governance.
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Compliance: Familiarity with compliance regulations (716, GDPR, GLBA, HIPAA).
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Knowledge of Microsoft Responsible AI principles.
SOFT SKILLS
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Strong communication skills with the ability to collaborate across technical and non-technical teams.
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Results driven; high integrity; ability to influence, negotiate and build relationships; superior communication skills; making complex decisions and leading teams through complex challenges.
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Self-disciplined to work in a virtual, agile, globally sourced team.
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Strategic, out-of-the-box thinker with problem-solving experience to assess, analyze, troubleshoot, and resolve issues.
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Excellent analytical skills, extraordinary attention to detail, and ability to present recommendations to business teams based on trends, patterns, and modern best practices.
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Experience with Power BI datasets and semantic modelling is an asset.
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Familiarity with Microsoft Purview or similar governance tools is an asset.
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Working knowledge of Python, PySpark, or KQL is an asset.
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Experience and passion for technology and providing exceptional experience both internally for employees and externally for clients and prospects.
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Strong ownership, bias to action, and ability to succeed in ambiguity.
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Ability to deliver value consistently by motivating teams towards achieving goals.
Certifications Requirements:
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AWS Certified Machine Learning – Specialty
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Microsoft Azure AI Engineer Associate
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Google Cloud Professional Machine Learning Engineer
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Coursera or Edureka AI/ML certifications (e.g., DeepLearning.AI’s Generative AI Specialization)
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ITIL or TOGAF for enterprise architecture alignment (optional)
DOMAIN KNOWLEDGE
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Experience in industries like healthcare, finance, or technology, with an understanding of relevant use cases.
New Vision is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees
