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.

2. 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.

3. 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.

4. 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

- Strong 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.
- Robust consulting mindset with structured problem-solving skills.
- Experience leading customer workshops and technical strategy sessions.

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