3 days ago
Remote, India or Bengaluru, IndiaSenior
Responsibilities
- Act as the technical and delivery interlock for assigned AI opportunities from qualified intake through solution approval, contracting, and handover.
- Shape end-to-end AI solution architectures across application, model, data, integration, infrastructure, cloud, and security domains.
- Lead or coordinate prototypes and proofs of concept to validate model performance, data, integration, security, scalability, latency, and cost assumptions.
- Define delivery approaches, workstreams, milestones, dependencies, governance, resource models, effort estimates, and mobilisation plans.
- Validate commercial constructs, pricing, rate-card assumptions, third-party costs, consumption charges, contingency, contribution, and margin.
- Support proposals and statements of work covering scope, deliverables, assumptions, exclusions, acceptance criteria, and client responsibilities.
- Assess technical, data, security, privacy, legal, commercial, and delivery risks and present evidence-based assurance recommendations.
- Lead transition to delivery and maintain the approved architecture, scope, commercial baseline, resource plan, risks, and client commitments.
- Develop reusable AI architectures, delivery patterns, prototypes, estimation models, and proposal assets while coaching regional teams.
Requirements
- Bachelor’s degree or equivalent practical experience in computer science, engineering, data science, information systems, business, or a related field.
- Extensive experience in enterprise solution architecture, technical consulting, AI engineering, or complex technology delivery, preferably in large-scale technology services.
- Experience working across pre-sales and delivery, engaging senior clients, and translating business needs into viable technical solutions.
- Demonstrated experience designing or validating enterprise AI and generative AI solutions, including prototypes and enterprise-platform or data integration.
- Experience with major cloud platforms and their AI, data, integration, and security services.
- Strong knowledge of AI and generative or agentic AI patterns, including model selection and evaluation, RAG, prompting, grounding, data pipelines, APIs, and vector databases.
- Experience defining delivery scope, work breakdown structures, resource models, effort estimates, delivery plans, commercial models, pricing assumptions, or business cases.
- Experience drafting or reviewing statements of work, managing technical and delivery risks, supporting solution or deal-assurance reviews, and transitioning solutions into delivery.
- Working knowledge of software engineering, DevOps, MLOps, and LLMOps, with strong understanding of security, privacy, compliance, data governance, and responsible AI.
- Relevant Microsoft Azure, Amazon Web Services, or Google Cloud Platform certification is preferred; TOGAF and additional AI, security, privacy, agile, project, or service-management certifications are advantageous.
- Experience with regulated environments and AI managed services, MLOps, or LLMOps is advantageous.
Benefits
- Remote working arrangement.
Tech Stack
Categories
Solutions Engineering