AI Solution Architect

Paradigmit Technology Services; Hyderabad; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Paradigmit Technology Services seeks an AI Solution Architect in Hyderabad to own end-to-end architecture for AI solutions from prototype to production, designing how models, data pipelines, apps and cloud infra fit together, while staying hands-on through deployment, monitoring and scale.

The role requires 4–7 years in software or AI engineering, strong Python and MLOps, with cloud deployment experience (GCP preferred). Office-based in Hyderabad's Hi-Tech City.

Qualifications

• B.E./B.Tech./M.Tech in CS, IT, Data Science, or related field
• 4 to 7 years in software or AI engineering with architecture ownership
• Proven record of taking at least one AI/ML project into live production
• Strong Python, plus one other production language
• LLM application patterns: RAG, agents, tool calling, prompt engineering
• MLOps: model and prompt versioning, automated evaluation, staged rollout
• Model serving design across batch, real-time, streaming with cost controls
• Cloud deployment experience, GCP preferred (Vertex AI, GKE, Cloud Run, BigQuery)
• API, microservice, and event-driven architecture design
• Data engineering fundamentals: pipelines, warehousing, data quality
• Security, access control, and data privacy in AI systems
• Strong communication and documentation to defend design decisions

Responsibilities

• Own the end-to-end architecture for AI solutions from prototype to production
• Design how models, data pipelines, applications, and cloud infra fit together
• Stay hands-on through deployment, monitoring, and scale

Skills

Python

LLM patterns

RAG

MLOps

Cloud deployment

GCP

Agent frameworks

REST APIs

Data pipelines

Distributed systems

Education

B.E./B.Tech./M.Tech in CS or IT

Tools

Docker

Kubernetes

Terraform

FastAPI

SQL

BigQuery

GitHub CI/CD

Job description

AI Solution Architect

Experience: 4 to 7 years

IMMEDIATE Joinners will be given a high Priority.

Work Mode: Office(5 Days)

Location: Hyderabad ( Hi-Tech City)

Job Description

Own the end to end architecture for AI solutions and take them from prototype into production. Design how models, data pipelines, applications, and cloud infrastructure fit together, then stay hands-on through deployment, monitoring, and scale.

Tech Stack

Python, LLMs and agent frameworks (Google ADK, LangGraph, LangChain), RAG, Vertex AI, GCP (GKE, Cloud Run, BigQuery, Pub/Sub, Cloud Storage), MLOps tooling, Docker, Kubernetes, Terraform, FastAPI, REST APIs, SQL, Vector databases, CI/CD

Requirements

• B.E./B.Tech/M.Tech in CS, IT, Data Science, or related field
• 4 to 7 years in software or AI engineering, with ownership of architecture for systems that reached production
• Proven record of taking at least one AI or ML project into live production use
• Strong Python, plus one other production language
• LLM application patterns: RAG, agents, tool calling, prompt engineering, evaluation harnesses
• MLOps practice: model and prompt versioning, automated evaluation, staged rollout, rollback, retraining triggers
• Model serving design across batch, real time, and streaming inference, with scaling and cost controls
• Cloud deployment experience, GCP preferred (Vertex AI, GKE, Cloud Run, BigQuery)
• API, microservice, and event-driven architecture design
• Data engineering fundamentals: pipelines, warehousing, data quality
• Security, access control, and data privacy as applied to AI systems
• Strong communication and documentation, able to defend a design decision with reasoning

Good to Have

• Google Cloud Professional Machine Learning Engineer or Professional Cloud Architect certification
• Experience with Gemini Enterprise and building agents in Gemini Enterprise
• Inference optimisation: quantisation, distillation, caching, batching, cost reduction
• Vector DB experience at scale (Vertex AI Vector Search, Pinecone, Qdrant, pgvector)
• Responsible AI controls: guardrails, human in the loop review, bias checks, auditability
• Domain exposure in government, public sector, education, agriculture, or law enforcement technology
• Experience leading a small team of engineers
• Open source contributions, publications, or conference talks in the AI space.