Gen AI Lead

ZK Technologies; Pune District; Hybrid; INR 2,000,000 - 3,000,000; Full time

Job summary

ZK Technologies in Pune, India is seeking an experienced GenAI Architect to design, develop, and deploy enterprise-grade GenAI systems. You will drive agentic frameworks, orchestrate multimodal models, and build scalable cloud-native architectures that deliver measurable business impact.

You will mentor teams, shape AI roadmap, and collaborate with stakeholders to translate business problems into scalable AI solutions.

Qualifications

• 7-12 years total experience with 3+ years in GenAI/LLM initiatives.
• Hands-on with agentic frameworks and orchestration runtimes.
• Deep knowledge of RAG, embeddings, and vector databases.
• Proficient in Python with async workflows and API integrations.

Responsibilities

• Architect and lead end-to-end GenAI solutions and agentic workflows.
• Design scalable AI pipelines using LangGraph, LangChain, and cloud AI services.
• Lead cross-functional teams across data, ML, product, and business.
• Establish best practices for safety, governance, and observability.
• Integrate GenAI with enterprise platforms and internal APIs.

Skills

GenAI leadership

Cross-functional collaboration

Prompt engineering

Responsible AI

Tools

LangGraph

LangChain

AgentCore

CrewAI

AutoGen

AWS Bedrock

Azure OpenAI

GCP Vertex AI

Pinecone

Weaviate

FAISS

Docker

Kubernetes

CI/CD

Python

Microservices

Observability tools

Memory / state management

Job description

NOTE:

Looking for immediate joiners.

Role Overview

You will lead the design, development, and deployment of enterprisegrade GenAI systems that go beyond traditional LLM pipelines. This role demands handson expertise with agentic frameworks, orchestration runtimes, multimodal models, and scalable cloud-native architectures. You will guide a cross-functional team to build production-ready AI solutions that deliver measurable business impact.

Key Responsibilities

• Architect and lead endtoend GenAI solutions including agentic workflows, autonomous decision loops, retrieval systems, and multimodel orchestration.
• Design scalable AI pipelines using modern frameworks such as LangGraph, LangChain, AgentCore, AWS Bedrock, and other emerging technologies (not limited to these).
• Build and optimize agents capable of tool use, memory, retries, humanintheloop checkpoints, and long-running workflows.
• Drive model strategy across open-source, proprietary, and cloud foundation models (Llama, Claude, GPT, Mistral, etc.).
• Lead cross-functional teams including data engineers, ML engineers, product managers, and business stakeholders.
• Establish best practices for prompt engineering, evaluation, safety, governance, and observability.
• Integrate GenAI systems with enterprise platforms (ServiceNow, Salesforce, internal APIs, data lakes, microservices).
• Mentor and upskill teams on agentic design patterns, RAG architectures, and production deployment.
• Stay ahead of the curve by evaluating new frameworks, runtimes, and model capabilities to continuously evolve the AI roadmap.

Required Skills & Experience

• 7-12 years total experience with 3+ years in leading GenAI/LLM initiatives.
• Strong hands-on experience with agentic frameworks (LangGraph, AgentCore, CrewAI, AutoGen, or equivalents).
• Deep understanding of LLM orchestration, state management, memory, and multi-step reasoning.
• Expertise in RAG, vector databases (Pinecone, Weaviate, FAISS), embeddings, and retrieval optimization.
• Proficiency with cloud AI platforms (AWS Bedrock, Azure OpenAI, GCP Vertex AI).
• Solid programming skills in Python, including async workflows, API integrations, and microservices.
• Experience deploying GenAI systems using Docker, Kubernetes, CI/CD, and observability tools.
• Strong understanding of AI safety, governance, evaluation metrics, and responsible AI principles.
• Ability to translate business problems into scalable AI architectures.

Preferred Skills

• Experience with multimodal models (vision, speech, structured data).
• Knowledge of MLOps and model lifecycle management.
• Exposure to enterprise automation platforms (ServiceNow, UiPath, Zapier, Make).
• Prior experience building AI agents for recruitment, HR, operations, or customer support is a plus.