Technical Architect

Iris Software, Inc.; India; On-site; INR 4,000,000 - 7,000,000; Full time

Benefits offered by this job

Benefits for Irisians

Job summary

Iris Software, Inc. is seeking a GenAI Architect to design, build, and lead enterprise GenAI solutions. You will work across business, data/ML, and engineering teams to deliver scalable, secure, and production-ready platforms, including RAG pipelines, vector databases, and agentic workflows.

The role demands hands-on implementation, strong architectural skills with LLMs, embeddings, and cloud-native deployments, plus experience with CI/CD and responsible AI practices.

Qualifications

• 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 Q&A, copilots, summarisation, search, workflow automation, knowledge assistants).
• Understanding of evaluation techniques: groundedness, relevance, faithfulness, latency/cost trade-offs.

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.

Skills

GenAI/LLM Architecture

Vector Databases

Agentic AI Frameworks

Tools

Pinecone

FAISS

Weaviate

Chroma

Milvus

Azure AI Search

Elastic (vector)

Job description

Job Description

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.

Key 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 productionisation using CI/CD pipelines, containerisation 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 Q&A, copilots, summarisation, 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

Strong working knowledge of agentic frameworks such as:

• LangChain
• LangGraph
• MCP
• Tool calling / function calling, memory, planning, multi‑agent workflows, guardrails

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 Productionisation

• 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.

Data Science and Machine Learning - Data Science and Machine Learning - Gen AI

Beh - Communication

Development Tools and Management - Development Tools and Management - CI/CD

Perks and Benefits for Irisians

Iris provides world‑class benefits for a personalised employee experience. These benefits are designed to support financial, health and well‑being needs of Irisians for a holistic professional and personal growth. Click here to view the benefits.