Job Title: Technical Architect

Job Summary

We are looking for a GenAI Architect to design, build, and guide the implementation of Generative AI solutions across enterprise use cases. The role requires strong hands-on and architectural expertise in LLMs, RAG, vector databases, and agentic AI frameworks, along with experience in cloud-native deployments and CI/CD automation. You will collaborate with business stakeholders, data/ML teams, and engineering teams to deliver scalable, secure, and production-ready GenAI platforms and applications.

Responsibilities

- Architect and design end-to-end GenAI solutions including RAG pipelines, agentic workflows, and multimodal use cases.
- Translate business problems into GenAI-driven use cases, solution blueprints, and implementation roadmaps.
- Design and implement retrieval systems using embeddings, chunking strategies, metadata filters, reranking, and evaluation metrics.
- Select and integrate vector databases and optimize indexing, retrieval performance, and relevance tuning.
- Build agentic systems using frameworks such as LangChain, LangGraph, and related orchestration tools.
- Define cloud architecture patterns for scalable, secure, and reliable GenAI deployments.
- Drive productionization using CI/CD pipelines, containerization using best practices.
- Ensure responsible AI practices including security, governance, privacy, compliance, and monitoring.
- Provide technical leadership, reviews, mentoring, and best-practice guidance to engineering teams.
- Collaborate with product and delivery teams to ensure solution alignment with timelines and business outcomes.

Primary Skills (Must Have)
- GenAI / LLM Architecture Algorithms
- Strong knowledge of GenAI algorithms and LLM concepts: prompting,



fine-tuning vs RAG, embeddings, context windows, token limits, hallucination control.
- Experience designing enterprise GenAI use cases (document QA, copilots, summarization, search, workflow automation, customer support, knowledge assistants).
- Understanding of evaluation techniques: groundedness, relevance, faithfulness, latency/cost trade-offs.
- Vector Databases Retrieval Systems
- Hands-on understanding of vector databases and similarity search concepts: embeddings, indexing, ANN search, hybrid search, metadata filtering
- Experience with tools like Pinecone, FAISS, Weaviate, Chroma, Milvus, Azure AI Search / Elastic (vector) (any relevant combination).
- Agentic AI Frameworks
- Solid working knowledge of agentic frameworks such as: LangChain LangGraph MCP Tool calling / function calling, memory, planning, multi-agent workflows, guardrails
- Cloud Architecture Deployments
- Experience with cloud services and architecture for GenAI workloads: compute, networking, storage, IAM/security, logging/monitoring
- Knowledge of cloud components supporting AI/ML solutions (managed services preferred).
- CI/CD and Productionization
- Experience implementing CI/CD pipelines for GenAI apps and services.
- Strong understanding of deployment patterns: containers (Docker), orchestration (Kubernetes), API deployment, model endpoint integration
- Familiarity with DevOps/MLOps practices: testing, observability, rollback, scaling, cost controls.

Mandatory Competencies
- Data Science and Machine Learning - Data Science and Machine Learning - Gen AI
- Beh - Communication
- Development Tools and Management - Development Tools and Management - CI/CD
- Cloud - GCP - Apigee API Management, API Gateway

Location

Location: Noida

Company: Iris Software

📌 Technical Architect (Noida)
🏢 IRIS SOFTWARE
📍 Noida