Solution Architect – Python with GenAI

EPAM Systems, Inc.; Bengaluru; On-site; INR 3,500,000 - 7,000,000; Full time

Job summary

EPAM Systems, Inc. is seeking a Solution Architect with deep Python and GenAI production experience to design and roll out enterprise GenAI solutions that run reliably in production.

You will lead architectural explorations, build reusable assets, mentor teams, and shape standards for LLM-powered apps while collaborating with multiple client teams and cloud platforms (AWS/Azure/GCP).

Qualifications

• 9–14 years of software development and solution architecture experience.
• Advanced Python for production-grade systems.
• Experience with microservices, design patterns, FastAPI.
• Hands-on GenAI applications including Agents, MCP, RAG, Agentic RAG and GraphRAG.
• Cloud environments AWS, Azure or GCP.
• Containerization with Docker and Kubernetes.
• Vector DBs Pinecone, Weaviate or Chroma.
• LLMOps principles and monitoring for production.

Responsibilities

• Develop end-to-end GenAI architecture solutions for enterprise needs, including RAG, Agents, and Multi-Agent designs.
• Build reusable accelerator assets and reference solutions for delivery consistency.
• Lead technical discovery meetings and support engineering teams on the ground.
• Establish evaluation frameworks and standards for LLM-based applications.
• Ensure scalable and manageable architectural approaches across teams.
• Engineer microservices systems using modern frameworks.
• Oversee integration with cloud infrastructure across AWS/Azure/GCP.
• Roll out LLMOps tools and monitoring for production quality.
• Mentor staff and share insights within the architecture community.

Skills

GenAI architecture

Python production code

Leadership

Client-facing

LLMOps

RAG

Agents and GraphRAG

LangChain

Cloud platforms

Production-grade microservices

Tools

Docker

Kubernetes

FastAPI

Redis

Elasticsearch

Kafka

Pinecone

Weaviate

Chroma

LangChain

LangGraph

Job description

Our growing organization needs a Solution Architect with expertise in Python and Generative AI to engineer and roll out enterprise-level GenAI solutions capable of operating reliably in production. Within our Solution Architecture team, you'll steer technical exploration projects, develop reusable building blocks, and contribute to shaping standards for LLM-powered applications.

Responsibilities

• Develop end-to-end GenAI architecture solutions, covering RAG approaches, Agents, and Multi-Agent designs, tailored to large enterprise needs.
• Build reusable accelerator assets and reference solutions to create consistency across delivery.
• Head up technical discovery meetings and give practical, on-the-ground support to engineering teams.
• Establish evaluation frameworks and outline standards guiding the creation of LLM-based applications.
• Work with multiple teams to confirm that architectural approaches remain scalable and manageable over the long haul.
• Engineer microservices systems by drawing on recognized patterns and modern technology frameworks.
• Oversee integration efforts tied to cloud infrastructure across platforms such as AWS, Azure, or GCP.
• Handle container deployment and orchestration responsibilities via Docker and Kubernetes.
• Assess and determine fitting vector database technologies, like Pinecone, Weaviate, or Chroma, to power GenAI solutions.
• Roll out LLMOps techniques and monitoring systems to elevate the quality of production releases.
• Aid in prompt engineering work and RAG evaluation to strengthen application dependability.
• Take part in mentoring programs and share insights across the architecture community.
• Maintain rigorous coding practices for Python applications intended for production use.
• Guide ongoing efforts to enhance system architecture design and solution scalability.

Requirements

• Experience spanning 9 to 14 years in software development, with strong expertise in solution architecture and system design.
• Advanced Python capabilities focused on writing code fit for production environments.
• Skill set covering microservices architecture, design patterns, FastAPI, Redis, Elasticsearch, and Kafka.
• Deep hands-on experience creating GenAI applications, including Agents, MCP, RAG, Agentic RAG, and GraphRAG.
• Strong knowledge of LangGraph, LangChain, and other orchestration technologies.
• Practical background evaluating LLMs and RAG frameworks, along with handling prompt management tasks.
• Proven capability delivering GenAI applications that scale effectively in production.
• Direct experience with cloud environments, including AWS, Azure, or GCP.
• Familiarity with containerization approaches, particularly Docker and Kubernetes.
• Understanding of vector database platforms, such as Pinecone, Weaviate, or Chroma.
• Solid comprehension of LLMOps principles and the monitoring tools tied to them.
• Strong leadership abilities appropriate for directing engineering teams.
• Capability to work in client-facing scenarios while collaborating effectively with teams.
• Solid English language skills, both spoken and written, at a B2 level or higher.

Nice to have

• Experience with conventional machine learning techniques, including feature engineering, model training, and evaluation.
• Familiarity with knowledge graph principles and fine-tuning strategies.
• Previous exposure to consulting work or roles centered on direct client interaction.