Solution Architect Python

EPAM Systems; Hyderabad; On-site; INR 4,000,000 - 9,000,000; Full time

Job summary

EPAM Systems in Hyderabad is seeking an experienced Solution Architect to design production-grade GenAI solutions for enterprise clients. You will lead technical discovery, develop reusable accelerators, and define best practices for LLM applications.

Responsibilities include building end-to-end GenAI architectures (RAG, Agents, Multi-Agent), guiding engineering teams, and ensuring scalable, cloud-native delivery with Docker, Kubernetes, and modern frameworks.

Qualifications

• 9–14 years of software development and architecture experience.
• Production-grade Python development experience.
• GenAI application development with agents, RAG, and multi-agent concepts.
• Strong knowledge of microservices, design patterns, and cloud architectures.
• Experience with LangGraph / LangChain and orchestration frameworks.
• Familiarity with LLM evaluation, prompt management, and MLOps practices.

Responsibilities

• Design end-to-end GenAI architectures for enterprise clients (RAG, Agents, Multi-Agent).
• Build reusable accelerators and reference implementations to standardize delivery.
• Lead technical discovery sessions and guide engineering teams.
• Define evaluation frameworks and best practices for LLM applications.
• Collaborate with cross-functional teams to ensure scalable, maintainable designs.
• Implement microservices architectures using modern frameworks.
• Oversee cloud integration (AWS/Azure/GCP) and containerization with Docker/Kubernetes.
• Evaluate vector databases like Pinecone/Weaviate/Chroma for GenAI.
• Apply LLMOps practices and monitoring to production deployments.
• Support prompt management and RAG evaluation for accuracy.
• Mentor and share knowledge within the architecture group.
• Ensure production-grade code quality in Python applications.
• Drive continuous improvement in system design and scalability.

Skills

Solution architecture

Python

GenAI

Microservices

Cloud platforms (AWS/Azure/GCP)

LLMOps

RAG

Team leadership

English communication

Tools

Docker

Kubernetes

FastAPI

Redis

Elasticsearch

Kafka

LangGraph

LangChain

Job description

Job Description:

We are seeking a Solution Architect specializing in Python and GenAI to design and build production-grade GenAI solutions for enterprise clients.

Join our Solution Architecture team to lead technical discovery, develop reusable accelerators, and define best practices for LLM applications.

Responsibilities

• Design end-to-end GenAI architectures including RAG, Agents, and Multi-Agent Systems for enterprise clients


• Build reusable solution accelerators and reference implementations to standardize delivery


• Lead technical discovery sessions and provide hands-on guidance to engineering teams


• Define evaluation frameworks and establish best practices for large language model (LLM) applications


• Collaborate with cross-functional teams to ensure scalable and maintainable system designs


• Implement microservices architectures using design patterns and modern frameworks


• Oversee integration of cloud infrastructure components such as AWS, Azure, or GCP


• Manage containerization and orchestration using Docker and Kubernetes


• Evaluate and select vector databases like Pinecone, Weaviate, or Chroma for GenAI solutions


• Apply LLMOps practices and monitoring to optimize production deployments


• Support prompt management and RAG evaluation to enhance application accuracy


• Contribute to knowledge sharing and mentoring within the architecture group


• Ensure production-grade code quality in Python applications


• Drive continuous improvement in system design and solution scalability

Requirements

• Strong solution architecture and system design experience with 9-14 years in software development


• Expert Python programming skills with production-grade code quality


• Proficient in microservices, design patterns, FastAPI, Redis, Elasticsearch, and Kafka


• Extensive GenAI application development experience including Agents, MCP, RAG, Agentic RAG, and GraphRAG


• Deep knowledge of LangGraph, LangChain, and orchestration frameworks


• Practical experience in LLM evaluation, RAG evaluation, and prompt management


• Proven track record delivering scalable, production GenAI applications


• Experience with cloud platforms such as AWS, Azure, or GCP


• Familiarity with container technologies Docker and Kubernetes


• Knowledge of vector databases including Pinecone, Weaviate, or Chroma


• Understanding of LLMOps practices and monitoring tools


• Excellent leadership skills to guide engineering teams


• Ability to work collaboratively in a client-facing environment


• Strong written and verbal English communication skills (B2+)

Nice to have

• Background in traditional machine learning including feature engineering, model training, and evaluation


• Experience with knowledge graphs and fine-tuning techniques


• Prior consulting or client-facing role experience