Senior AI Solutions Architect
New
J
JobgetherEnterprise AI
Based in IndiaFull-TimeSenior
Salary not disclosed
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Job Details
- Required Skills
- Microsoft AzureDevOpsPrompt EngineeringMLOpsGenerative AI
Requirements
- Bachelor's degree in Computer Science, Engineering, Data Science, Information Systems, Business, or a related field, or equivalent practical experience.
- Extensive experience in enterprise solution architecture, technical consulting, AI engineering, or complex technology delivery.
- Advanced knowledge of application, integration, data, infrastructure, cloud, and security architecture.
- Strong understanding of AI, generative AI, and agentic AI patterns, including model selection and evaluation, RAG, prompt engineering, grounding, data pipelines, APIs, vector databases, and orchestration frameworks.
- Experience with one or more major cloud platforms: Microsoft Azure, AWS, or Google Cloud Platform.
- Familiarity with software engineering, DevOps, MLOps, and LLMOps.
- Ability to develop or oversee prototypes, validate feasibility, and define paths from proof of concept to production solutions.
- Experience defining project scope, work breakdown structures, resource models, effort estimates, timelines, dependencies, governance, and mobilization plans.
- Practical experience with pricing models, rate-card assumptions, third-party costs, financial estimates, contribution margins, and business cases.
- Familiarity with statements of work, acceptance criteria, contractual assumptions, change control, and delivery risk management.
- Strong understanding of security, privacy, regulatory compliance, data governance, and responsible AI principles.
- Relevant Azure, AWS, or Google Cloud certifications are preferred; TOGAF and qualifications in AI, security, privacy, agile, project management, or service management are advantageous.
Responsibilities
- Act as the primary technical and delivery contact for assigned AI opportunities throughout the sales lifecycle.
- Define and validate end-to-end architectures across applications, AI models, data pipelines, integrations, cloud infrastructure, orchestration frameworks, and security.
- Lead or coordinate prototypes and proofs of concept to validate assumptions and reduce implementation risks.
- Develop delivery approaches covering workstreams, milestones, dependencies, resources, skills, delivery locations, and mobilization plans.
- Develop and validate pricing and cost models, effort estimates, cloud costs, contingency, cost recovery, and margin assumptions.
- Document deliverables, milestones, assumptions, exclusions, acceptance criteria, client responsibilities, and dependencies for proposals and statements of work.
- Evaluate solution completeness, delivery readiness, commercial consistency, and technical, security, privacy, legal, contractual, and operational risks.
- Maintain approved solution baselines and hand over architecture, scope, estimates, resource plans, assumptions, risks, and commitments to delivery teams.
- Develop reusable AI architectures, delivery patterns, prototypes, estimation models, proposal templates, and commercial frameworks.
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