Jr. Gen AI Engineer

Sourceright Technologies

4 - 5 years

13 - 17 Lacs

ahmedabad bengaluru

Posted:4 days ago| Platform:

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Skills Required

remediation spring boot backend orchestration linux architecture memory management system design application development python

Work Mode

Work from Office

Job Type

Full Time

Job Description

Must have skills : Gen AI, Python or JAVA, Vector DB, LLM, RAG, Linux OS fundamentalsGood to have : Communication, Network firewalls, routers and switches, Spring boot

Role: Gen AI Engineer - LLM Architecture, RAG, and Agentic AI

Requirements

4-5 years of relevant experience in AI/ML or backend systems, with a strong focus on LLM application development and AI system design.
Proven experience architecting and deploying AI frameworks or agentic systems in production environments.
Strong expertise in AI framework and the Java ecosystem comfortable building and orchestrating
AI workflows using Spring Boot, Spring AI, and vector storage integrations.
Experience in RAG architecture, prompt engineering, memory management, and model orchestration patterns.
Familiarity with OpenAI, Anthropic, and Google Gemini APIs and evaluation frameworks for reasoning quality.
Strong software engineering fundamentals versioning, pipelines, CI/CD, and API integration.
Experience with vector databases (eg, Pinecone, Weaviate, or Chroma) or knowledge graph-based retrieval.

Responsibilities

Architect and build Cognia s AI framework, integrating LLMs, memory, RAG, and agentic reasoning across the platform.
Design AI agents and reasoning flows that interpret app, infra, and IAM telemetry for
risk triage and remediation.
Develop retrieval-augmented generation (RAG) pipelines for context-based summarization,
persona-driven views, and continuous learning.
Implement LLM memory and context management (short-term, long-term, and episodic memory) for persistent reasoning.
Tune prompts, evaluation logic, and reasoning paths across multiple models (GPT-4, Claude, Gemini, etc).
Collaborate with founders, backend engineers, and data teams to integrate AI logic with product pipelines and APIs.
Define AI architecture standards, evaluation metrics, and observability for all LLM-driven components.

Bonus:

Background in cybersecurity, SaaS platforms, or risk intelligence systems.

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