Solution Architect – Python with GenAI

Epam Systems; Hyderabad, Bengaluru; On-site; INR 4,200,000 - 7,000,000; Full time

Job summary

Epam Systems is seeking a Solution Architect specializing in Python and GenAI to design and build production‑grade GenAI solutions for enterprise clients. You will lead technical discovery, develop reusable accelerators, and define best practices for LLM applications.

The role requires 9–14 years of software development experience, strong Python expertise, and proficiency in microservices, cloud platforms, and container tech. You will mentor teams and drive scalable system designs.

Qualifications

• 9–14 years of software development and architecture experience.
• Strong Python programming skills with production-grade code quality.
• Experience in microservices, design patterns and modern frameworks.
• GenAI applications including LLMs, RAG, and multi-agent architectures.
• Hands-on knowledge of cloud platforms (AWS/Azure/GCP) and DevOps tooling.
• Proficiency with containerization and orchestration (Docker, Kubernetes).

Responsibilities

• Design end-to-end GenAI architectures for enterprise clients.
• Build reusable accelerators and reference implementations.
• Lead technical discovery sessions and guide engineering teams.
• Define evaluation frameworks for LLM applications.
• Collaborate with cross-functional teams for scalable designs.
• Implement microservices architectures using modern patterns.
• Oversee cloud integration and container orchestration.
• Evaluate vector databases for GenAI solutions.
• Apply LLMOps practices and monitoring for production deployments.
• Support prompt management and RAG evaluation.
• Mentor and share knowledge within the architecture group.
• Ensure production-grade Python code and continuous improvement.

Skills

Python programming

GenAI

Microservices

Design patterns

FastAPI

Redis

Elasticsearch

Kafka

LLM evaluation

Cloud platforms

Docker

Kubernetes

LangGraph/LangChain

Production-grade code

Tools

Docker

Kubernetes

Redis

Elasticsearch

Kafka

FastAPI

LangChain

LangGraph

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