Key Responsibilities

Solution Architecture

- Design and oversee secure, scalable enterprise AI/ML and Generative AI (LLM/SLM) architectures.
- Define solution architecture for AI-powered applications and enterprise workflows.
- Design architectures covering RAG, LLMs, agentic AI, intelligent automation, and AI assistants.
- Ensure solutions meet requirements around security, scalability, reliability, performance, and cost.

AI Strategy &

- Governance

- Define AI reference architectures, technology roadmaps, and engineering standards.
- Establish AI governance frameworks covering Responsible AI, model deployment, security, and compliance.
- Evaluate emerging AI technologies and identify opportunities for enterprise adoption.

GenAI &

- Technology Integration

- Architect and implement use cases such as :
- Intelligent chatbots
- Automated document processing
- RAG applications
- Agentic workflows
- Enterprise AI assistants
- Work with LLMs, SLMs, vector databases, embeddings, and semantic search.
- Integrate modern GenAI orchestration frameworks into enterprise solutions.

Technical Leadership

- Establish and contribute to AI Centers of Excellence (CoE).
- Mentor and guide AI Engineers, ML Engineers, Data Scientists, Architects, and MLOps teams.
- Conduct architecture and technical design reviews.
- Define best practices for AI engineering, deployment, monitoring, and optimization.

Consulting &

- Pre-Sales





- Lead AI discovery and solutioning workshops with customers.
- Support RFP/RFI responses and technical solution proposals.
- Develop AI transformation roadmaps aligned with business objectives.
- Present AI architecture and transformation strategies to senior stakeholders.

Mandatory Skills

AI / ML

- Machine Learning &
- Deep Learning
- Generative AI / LLMs / SLMs
- RAG
- AI/ML Solution Architecture
- TensorFlow / PyTorch

GenAI Frameworks

- LangChain
- LangGraph
- Semantic Kernel
- Agentic AI

Cloud &

- MLOps

- Azure and/or AWS
- Cloud-native AI services
- Docker / Kubernetes
- CI/CD pipelines
- MLOps / LLMOps
- Model deployment and monitoring

Good To Have

- Vector databases
- Knowledge Graphs
- Semantic Search
- Prompt Engineering
- LLM Evaluation
- Responsible AI &
- AI Governance
- Azure OpenAI / Amazon Bedrock
- Enterprise AI security
- Experience building reusable AI accelerators and frameworks

Preferred Candidate Profile

- 5 - 10 years of overall IT experience with significant hands-on AI/ML architecture experience.
- Proven experience designing and delivering enterprise-scale AI/GenAI solutions.
- Strong understanding of cloud architecture and MLOps.
- Experience leading technical teams and mentoring AI/ML professionals.
- Solid consulting, communication, stakeholder-management, and presentation skills.
- Ability to translate business requirements into scalable AI solutions.

(ref:hirist.tech)

📌 Nous Infosystems - AI Specialist/Architect (Bengaluru)
🏢 Nous Infosystems
📍 Bengaluru