AI Engineer (Conversational Analytics & GenAI Systems)

IRISS, Inc.Bengaluru, Karnataka, India

15 days ago

Job description

Company Overview :

IRISS, Inc. is a leading innovator in the field of advanced technological solutions, providing cutting-edge products and services to enhance safety, reliability, and efficiency of assets across various industries. Our commitment to pushing boundaries and delivering exceptional solutions has positioned us as a trusted partner for clients seeking top-tier technical expertise in Condition Based Monitoring.

IRISS Inc - Leader in Electrical Maintenance Safety Solutions

IRISS - YouTube

Position : AI Engineer (Conversational Analytics & GenAI Systems)

Location : Bengaluru, India

About the Product :

You will work on IRISS's conversational analytics platform, a GenAI-powered chatbot that

transforms natural language queries into validated, compliant, and tenant-aware SQL and

visual insights. This platform enables users to ask business questions like “Show me last

month's motor temperature anomalies in Plant 3” and get immediate, accurate dashboards

and reports generated safely through AI-driven data pipelines.

Our AI stack :

Interprets user intent using LLMs.
Generates validated, policy-compliant SQL.
Executes and visualizes data with context and feedback loops.
Powers a RAG-based (Retrieval-Augmented Generation) framework integrated with

existing IoT and analytics microservices

Job Overview :

You will design, develop, and maintain the AI chatbot platform that serves as the

intelligence layer for our SaaS ecosystem. This includes architecting end-to-end

conversational pipelines from LLM prompt design to data retrieval, integrating vector-

based search systems and RAG pipelines into our service mesh, leveraging AWS AI / ML and

orchestration services such as Bedrock, Kendra, OpenSearch, Lambda, ECS, and S3 to build

scalable and secure infrastructure, and partnering with full-stack and front-end engineers

to embed AI features directly into user workflows

Backend :

ASP.NET Core with ABP & ASP.NET Zero modules, EF Core, and SQL Server for tenancy-

aware domain logic

Python (FastAPI / Flask) for new microservices and migration targets

APIs : SignalR hubs and REST endpoints exposed through the Web Host

Infrastructure :

AWS Services : ECS for container orchestration, RDS (Aurora) for databases, S3 for

storage, Lambda for serverless functions

Hangfire for background jobs, log4net + custom middleware for correlation-aware

logging

HealthChecks, Stripe + Firebase integrations

DevOps : AWS CDK-driven Infrastructure as Code with containerized services, Redis

caching, and microservice extensions

Frontend :

Angular 18 (latest version with standalone components support)

TypeScript 5.5

RxJS 7.4 for reactive programming

PrimeNG, Angular Material, ngx-charts for UI components

Key Responsibilities :

Design and implement backend services in .NET Core (ASP.NET Core Web API) using

Entity Framework Core and LINQ

Help migrate our backend APIs to Python microservices architecture

Build clean, testable Angular 18+ UIs and reusable components (standalone)

Design and evolve multi-tenant backend services for assets, sensors, work orders,

notifications, and AI workflows

Integrate data sources : SQL (SQL Server / Aurora) and InfluxDB for time-series telemetry

Implement background jobs, rate limiting, and observability using Hangfire, Redis, and log

enrichment patterns

Extend REST and SignalR endpoints while maintaining tenant isolation and role-based

access control

Collaborate with IoT and data teams to expose sensor data, alerts, reports, and analytics

Implement authentication / authorization, input validation, and error handling across the

stack

Participate in code reviews, ADRs, grooming, and release readiness checks

Contribute to CI / CD pipelines (GitHub Actions), basic observability, and performance

profiling

Define service boundaries, transactional integrity, and performance within core

application layers

Core Stack & Technologies

AI / ML & Data Intelligence

Python 3.10+ (FastAPI, LangChain, Haystack, or equivalent)

LLMs : OpenAI, Anthropic, Hugging Face, or open-source models (LLaMA, Mistral, Falcon)

RAG Systems : FAISS, Pinecone, OpenSearch Vector Store, or ChromaDB

Prompt Orchestration : LangChain, Semantic Kernel, or internal tooling

Data Validation & Safety : SQL sanitization layers and policy enforcement modules

Visualization Layer : Chart.js or D3.js integration for generated insights

Cloud & Infrastructure :

AWS Bedrock, Kendra, OpenSearch, Lambda, S3, CloudWatch, ECS, and EC2

API Gateway for AI microservices

Redis or DynamoDB for caching and conversation state

OpenTelemetry for observability

CI / CD using GitHub Actions, AWS CDK, and Docker-based microservices

Front-End & Integration

Works closely with Angular 18+ applications and .NET / Python backend microservices

Exposes APIs to the Full-Stack and Front-End teams for seamless user interactions

Implements real-time feedback mechanisms for model evaluation and tuning

Key Responsibilities :

Architect, develop, and maintain the GenAI chatbot platform from the ground up

Build multi-turn conversation flows and contextual memory for data queries

Implement RAG pipelines using vector databases and curated embeddings

Integrate open-source and commercial LLMs through APIs or local deployment

Create safety and compliance modules that validate SQL and policy rules before execution

Collaborate with backend engineers to design AI microservices that scale horizontally

Deploy, monitor, and optimize models using AWS Bedrock, Kendra, and OpenSearch

Maintain observability and feedback loops for improving model accuracy and reliability

Partner with front-end teams to deliver chat-first analytics interfaces

Contribute to documentation, testing, and architectural decision records for AI systems

Requirements :

Bachelor's or Master's degree in Computer Science, Data Science, or a related field

Minimum 3 years of experience developing and deploying AI-powered applications or

chatbots

Strong Python expertise (FastAPI, Flask, or Django for microservices)

Experience with LLM integration (OpenAI, Bedrock, Hugging Face, or local models)

Hands-on experience with AWS ecosystem including Bedrock, Kendra, OpenSearch, ECS,

Lambda, and CloudWatch

Deep understanding of RAG architecture, vector databases, and embeddings-based

retrieval

Knowledge of prompt design, model orchestration, and AI safety validation

Familiarity with SQL and multi-tenant data systems

Experience with Docker, Git-based CI / CD, and microservice architectures

Nice-to-Have

Experience fine-tuning or hosting open-source LLMs (LLaMA, Mistral, Falcon)

Understanding of LangChain Agents or Semantic Kernel pipelines

Familiarity with Angular and .NET ecosystems for end-to-end integration

Exposure to observability frameworks such as OpenTelemetry, Prometheus, or Grafana

Knowledge of enterprise data governance and AI compliance frameworks

Contributions to open-source AI projects or custom LLM integrations

What You'll Work On :

Migration of .NET Core backend services to Python microservices

Tenant-aware APIs powering asset hierarchies, predictive maintenance, and automated

work orders

Real-time dashboards and notifications for sensor events, alerts, and chat integration

Performance and reliability for data-heavy dashboards (pagination, caching, change

detection)

Background workflows orchestrating AI-driven insights and report exports

REST services consumed by Angular dashboards and mobile clients

Observability hooks (health checks, telemetry, correlation IDs) for enterprise-grade

reliability

Developer experience improvements (codegen, linting, templates, better local envs)

What You Will Build :

A conversational analytics chatbot capable of generating real-time, compliant SQL queries

RAG pipelines that fetch and embed domain knowledge across tenants

Context-aware AI microservices integrated with IRISS’s monitoring and reporting systems

Evaluation dashboards for prompt performance, latency, and query accuracy

Continuous learning and feedback loops to improve the GenAI system over time

Development Environment

Python 3.10+, FastAPI, LangChain

AWS Bedrock, OpenSearch, Kendra, Lambda, ECS

Angular 18+ for embedded UIs

Node.js 16+, Yarn, VS Code

GitHub Actions and AWS CDK for CI / CD

Dockerized microservices architecture

Compensation :

Competitive salary, benefits, and strong growth opportunities.

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Conversational Ai Engineer • Bengaluru, Karnataka, India

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