Role & responsibilities

1. Pre-Sales Technical Leadership

- Act as the primary technical advisor during AI/ML and Generative AI sales engagements.
- Partner with sales, account management, and business development teams to qualify opportunities.
- Conduct customer discovery workshops to understand:

- Business goals

- Current technology stack

- Data maturity

- Security and compliance requirements

- Operational constraints
- Translate customer requirements into scalable AI/ML solution architectures.

- Lead technical responses for

- RFPs/RFIs

- Proposals

- Statements of Work (SOWs)

- Technical estimations
- Present AI solution strategies, demos, proof-of-concepts, and architecture walkthroughs to technical and executive stakeholders.
- Communicate business value, ROI, implementation risks, and trade-offs to both technical and non-technical audiences.

1. AI/ML & Generative AI Solution Architecture

- Design end-to-end AI/ML and Generative AI systems from concept through production deployment.

- Define

- Data pipelines

- Feature engineering strategy

- Model architecture

- Vector database design
- RAG (Retrieval-Augmented Generation) frameworks

- Agentic AI workflows

- Integration patterns
- Architect scalable cloud-native AI solutions on platforms such as:

- Amazon Web Services

- Microsoft Azure

- Google Cloud Platform

- Evaluate and recommend appropriate

- LLM providers

- AI tooling

- Frameworks

- Infrastructure
- Build-vs-buy approaches
- Ensure solutions align with enterprise security, governance, and compliance standards.

1. Customer Engagement & Delivery Oversight

- Work closely with customer engineering and product teams post-sale.
- Lead architecture reviews and technical decision-making workshops.

- Produce

- Architecture diagrams

- Technical design documents





- Decision records

- Deployment strategies

- Guide implementation teams through

- Development milestones

- Technical blockers

- Design evolution

- Performance optimization
- Support successful production deployment and operational readiness.

1. Technical Strategy & Innovation

- Stay current with advancements in:

- Generative AI

- LLMs

- AI agents

- MLOps/LLMOps

- Cloud AI services
- Contribute to internal AI best practices, reusable accelerators, and reference architectures.
- Evaluate emerging frameworks such as:

- LangChain

- LangGraph

- Provide thought leadership through

- Technical blogs

- Whitepapers

- Conference presentations

- Internal enablement sessions

Preferred candidate profile Technical Expertise The ideal candidate should possess:

- 10+ years of experience in software engineering, AI/ML, and enterprise solution architecture.
- Deep hands-on expertise in:

- Machine Learning

- Generative AI

- LLM applications

- RAG systems

- Agentic workflows
- Solid practical experience with cloud AI ecosystems including:

- Amazon Web Services Bedrock

- Microsoft Azure OpenAI
- Google Cloud Platform Vertex AI

- Strong understanding of

- Distributed systems

- APIs and integrations
- Cloud-native architectures

- Scalable backend systems

- Security and governance

Customer & Consulting Capability The preferred candidate should demonstrate:
- Excellent communication and presentation skills.
- Strong stakeholder management capabilities with experience engaging:

- Engineering teams

- Product leadership

- Senior executives
- VP/C-suite stakeholders
- Ability to simplify complex AI concepts into business outcomes.
- Strong consulting mindset with structured problem-solving skills.
- Experience leading customer workshops and technical strategy sessions.

📌 AI Solutions Architect (Bengaluru)
🏢 Infinites Hr Services Pune
📍 Bengaluru