Artificial Intelligence Specialist

EdgeVerve; Bengaluru; In loco; INR 4.000.000 - 7.000.000; Tempo pieno

Descrizione del lavoro

EdgeVerve Systems Limited in Bengaluru seeks an experienced AI Architect to lead GenAI and agentic AI delivery, shaping architecture from design to production deployment. You will mentor engineers, set standards for prompt engineering, and partner with product, data and security teams to ensure secure, scalable solutions.

The role requires hands-on GenAI delivery with LLMs, RAG, and tool orchestration, along with strong communication to clients and stakeholders.

Competenze

• Strong hands-on experience architecting Generative AI solutions with LLMs, RAG, embeddings and prompt engineering.
• Experience building agentic AI solutions with multi-agent orchestration and tool calling.
• Understanding of Claude, Gemini, OpenAI models and trade-offs.
• Proficiency in Python and Java Spring Boot; microservices and REST APIs.
• Cloud experience (AWS, Azure, GCP), Docker, Kubernetes, CI/CD.

Mansioni

• Design end-to-end GenAI and agentic AI architectures for the AINext platform.
• Lead framework selection, guardrails, security, and production deployment.
• Provide technical leadership and mentorship to Gen AI engineers and tech leads.
• Set standards for prompt engineering, evaluation, and deployment of GenAI solutions.
• Guide IDP-style architectures including ingestion, extraction and integration (nice-to-have).
• Engage with clients as the technical lead and support pre-sales with designs and estimates.

Conoscenze

GenAI Architecture

Agentic AI

LLM expertise

RAG & Vector Search

Python

Java Spring Boot

Microservices

Cloud platforms

Docker

Kubernetes

REST APIs

CI/CD

LangChain

LangGraph

Google ADK

Claude

Gemini

OpenAI models

EdgeVerve Systems

Strumenti

LangChain

LangGraph

Google ADK

Claude

Gemini

OpenAI models

Docker

Kubernetes

REST APIs

CI/CD

Descrizione del lavoro

Experience: 12 to 18 Years

Role Summary:

Accountable for architecting and leading the delivery of AI-led AINext solutions built on Generative AI and Agentic AI. Owns solution architecture, model and framework selection, and technical quality for GenAI use cases, from design and prototyping through production deployment.

Key Responsibilities:

• Design end-to-end architecture for Generative AI and Agentic AI solutions on the AINext platform, covering LLM selection, RAG pipelines, agent orchestration, tool integration, and guardrails.
• Architect agentic and multi-agent workflows using frameworks such as LangChain, LangGraph, and Google ADK.
• Design backend and integration layers using Python and Java Spring Boot, exposing AI capabilities as secure and scalable services.
• Evaluate and recommend foundation models (for example Claude, Gemini, and OpenAI models) and deployment options for client problem statements.
• Design IDP (Intelligent Document Processing) solution architecture end to end, combining document ingestion, classification, OCR and layout-aware extraction, LLM-based extraction, validation, human-in-the-loop review, and downstream integration (good to have).
• Provide technical leadership and mentorship to Gen AI engineers, Python developers, and technology leads.
• Establish standards for prompt engineering, agent design, evaluation, and deployment of GenAI solutions.
• Conduct design and code reviews, and ensure reliability, security, scalability, and performance of AI solutions.
• Troubleshoot and resolve technical issues in production AI systems.
• Define evaluation, observability, cost, and latency monitoring approaches for LLM and agent-based solutions.
• Assess ethical implications, data privacy, and security risks of GenAI deployments, and design appropriate guardrails.
• Act as the primary technical contact for client architects and business stakeholders, and drive GenAI architecture workshops.
• Advise clients on GenAI and agentic AI use cases and provide feedback for the product roadmap.
• Partner with product architecture, data, engineering, and security teams.
• Support pre-sales activities by providing GenAI solution design, estimates, and expertise.
• Technical Leadership & Quality:
• Evaluation, Governance & Responsible AI:
• Stakeholder Management & Pre-sales:

Required Qualifications and Skills

• Strong hands-on experience architecting Generative AI solutions using LLMs, RAG, embeddings, vector databases, and prompt engineering.
• Experience building agentic AI solutions, including tool calling, multi-agent orchestration, memory, and planning, using LangChain, LangGraph, Google ADK, or similar frameworks.
• Experience with foundation models such as Claude, Gemini, and OpenAI models, and an understanding of their trade-offs.
• Strong programming skills in Python and Java Spring Boot, with experience in microservices and REST APIs.
• Experience with cloud platforms (AWS, Azure, or GCP), Docker, Kubernetes, and CI/CD.
• Strong understanding of data structures, databases (SQL and vector), and the AI project lifecycle.
• Understanding of AI security, data privacy, and responsible AI practices.
• Excellent communication skills and the confidence to convey technical advice and guidance to clients.
• Good to have: Understanding of end-to-end Intelligent Document Processing (IDP) solution architecture, including document ingestion, extraction, validation, and integration.

Key Skills:

• Generative AI & Agentic AI Architecture
• RAG & Vector Search
• Solution Design & Technical Leadership
• Responsible AI & Guardrails
• Client & Stakeholder Management
• Architects with hands-on Generative AI and agentic AI delivery experience who have designed and deployed LLM-based solutions and led client architecture discussions.
• 12 to 18 years total, with recent hands-on experience in GenAI or agentic AI solution design.
• AI Architect, Gen AI Architect, Gen AI Specialist, Solution Architect (AI), Agentic AI Architect, AI Solutions Architect.
• Generative AI, LLM, RAG, vector database, embeddings, prompt engineering, agentic AI, multi-agent, LangChain, LangGraph, Google ADK, Claude, Gemini, Python, Java, Spring Boot, microservices, cloud (AWS, Azure, GCP), Docker, Kubernetes.
• Good to have: IDP.
• Hands-on GenAI delivery with RAG, plus experience with at least one agent framework (LangChain, LangGraph, or Google ADK).
• Good to have: cloud architect or cloud AI certifications (AWS, Azure, GCP).
• Classic ML-only or analytics profiles with no GenAI or agentic AI delivery, or architect profiles with no hands-on exposure to LLM applications.