Senior Generative Architect

Coforge; Hyderabad, Pune District; Hybrid; INR 4,000,000 - 7,000,000; Full time

Job summary

Coforge is seeking an experienced AI/ML architect to lead the design, development, and deployment of Generative AI and LLM-based solutions across enterprise use cases. The role emphasizes building scalable architectures, RAG pipelines, and agent-driven workflows with production-grade quality.

The candidate will collaborate with data scientists, ML engineers, and product teams, ensuring alignment with governance, ethics, and performance requirements across cloud and hybrid environments.

Qualifications

• 7+ years of experience in AI/ML architecture.

Responsibilities

• Develop and maintain the architectural framework for generative AI and LLM-powered solutions.

Skills

Python

Java

C++

LLMs

Prompt engineering

Embeddings

TensorFlow

PyTorch

Hugging Face

LangChain

LangGraph

Semantic Kernel

Vector databases

FAISS

Pinecone

Weaviate

Azure AI Search

API integration

Microservices

AWS

Azure

GCP

RAG pipelines

Knowledge pyramids

Tools

LangChain

LangGraph

Semantic Kernel

TensorFlow

PyTorch

Hugging Face

FAISS

Pinecone

Weaviate

Azure AI Search

Job description

Key Responsibilities:

Architecture Design:

• Develop and maintain the architectural framework for generative AI and LLM-powered solutions (including RAG, agents, and multi-step workflows), ensuring alignment with business goals and technical standards.

Model Development:

• Lead the design, fine-tuning, and deployment of generative AI models (e.g., GPT, DALL-E, Stable Diffusion) and LLM-based applications for use cases such as content generation, automation, copilots, and decision support systems.

LLM Orchestration & Agent Design:

• Design and implement LLM orchestration pipelines using frameworks such as LangChain, LangGraph, Semantic Kernel, or similar tools. Build agentic workflows, tool-augmented agents, and multi-step reasoning pipelines.

RAG & Knowledge Systems:

• Architect and implement Retrieval-Augmented Generation (RAG) pipelines using vector databases (e.g., FAISS, Pinecone, Azure AI Search) and embedding models to enable enterprise knowledge integration.

Integration:

• Collaborate with software engineering teams to integrate generative AI capabilities, LLM services, nd autonomous agents into existing applications, APIs, and enterprise workflows.

Scalability & Performance:

• Ensure AI/LLM solutions are scalable, optimized for latency and cost, and production-ready across cloud and hybrid environments.

Ethical AI Practices:

• Implement and enforce ethical guidelines for AI development, including bias mitigation, explainability, safety guardrails, and responsible AI practices in LLM deployments.

Research & Innovation:

• Stay abreast of advancements in LLMs, prompt engineering, agent frameworks, multimodal AI, and GenAI tooling, incorporating emerging techniques into the AI strategy.

Collaboration:

• Work closely with data scientists, ML engineers, product teams, and stakeholders to identify AI-driven opportunities and ensure successful delivery.

Documentation & Standards:

• Create comprehensive documentation for AI architectures, LLM workflows, prompt templates, and best practices. Establish coding, evaluation, and governance standards.

Experience:

• Minimum of 7+ years of experience in AI/ML architecture or a related role.
• Proven experience in designing and deploying Generative AI and LLM-based solutions in production.
• Hands-on experience with LangChain, LangGraph, or similar orchestration frameworks for building LLM pipelines and agent systems.
• Experience in building RAG pipelines RAG pipelines RAG pipelines, vector database integration, and prompt engineering techniques.
• Strong experience with AI frameworks/tools such as TensorFlow, PyTorch, Hugging Face.
• Experience with cloud platforms (AWS, Azure, GCP) and deploying scalable AI/LLM solutions.

Technical Skills:

• Proficiency in programming languages such as Python (preferred), Java, or C++
• Strong expertise in LLMs, prompt engineering, embeddings, and fine-tuning techniques
• Hands-on experience with:
• LangChain, LangGraph / Agent frameworks
• Vector databases (Pinecone, FAISS, Weaviate, Azure AI Search)
• API integration & microservices architecture
• Strong understanding of machine learning and deep learning concepts