Job Description

Role; GEN AI

Notice Period - Immediate - 90 Days

About the Role:

The ideal candidate should have a solid background in

backend development, AI/ML orchestration, and data management

, with hands-on experience in

Generative AI and Agentic AI frameworks

. They will be responsible for

building REST APIs

, managing

data pipelines

, and implementing

AI/ML solutions

such as

retrieval-augmented generation (RAG)

,

intent detection models

, and

autonomous AI agents

to deliver intelligent, production-ready solutions.

Key Responsibilities:

Backend Development

: Design, develop, and maintain REST APIs using Python to integrate AI/ML services.

Agentic AI Development

: Design and orchestrate

AI agents

capable of reasoning, planning, and executing multi-step tasks by integrating LLMs with APIs, tools, and data sources.

AI/ML Orchestration

: Implement and manage machine learning models, large language models (LLMs), Agentic AI workflows and AI orchestration using Python.

Generative AI Solutions

: Using Generative AI models for tasks like text generation, summarization, conversational AI, and content creation.

Data Management

: Work with structured databases (SQL), graph databases (e.g., CosmosDB), and unstructured data stores (e.g., Elasticsearch).

RAG Implementation

: Build retrieval-augmented generation (RAG) pipelines leveraging Azure AI Search, AWS OpenSearch, or other vector databases for contextual responses.

Data Pipelines

: Design and manage robust data ingestion and transformation pipelines to feed AI models.

Intent Detection & NLU

: Develop or integrate intent detection models and natural language understanding (NLU) solutions to enhance conversational AI.

Prompt Engineering & Optimization

: Create and optimize prompts for LLMs to improve response quality and reduce latency.

AI Integration

: Collaborate with frontend and product teams to embed Gen AI features into enterprise applications.

Required Skills:

Backend Development

: Proficient in Python for building and maintaining scalable REST APIs, familiarity with integrating AI services.

AI/ML Orchestration

: Strong expertise in Python with a focus on machine learning, large language models (LLMs), AI orchestration.

Agentic AI Expertise:

Experience in building

autonomous AI agents

using frameworks like OpenAI Functions, or custom orchestration solutions to handle tool use and multi-step reasoning.

Generative AI Expertise

: Generative AI models (text generation, summarization, conversational AI) and applying prompt engineering techniques.

Data Management

: Solid understanding of structured (SQL), graph (CosmosDB), and unstructured (Elasticsearch) databases; ability to design efficient data access patterns for AI workloads.

RAG Implementation

: Proven experience implementing retrieval-augmented generation using Azure AI Search, AWS OpenSearch, or other vector databases.

Data Pipelines

: Hands-on experience building and managing data ingestion and transformation pipelines using Databricks, Azure Data Factory, or equivalent tools.

Intent Detection & NLU

: Skilled in developing or deploying intent detection models and natural language understanding (NLU) components for conversational AI applications.

Preferred Skills:

Familiarity with cloud platforms such as Azure and AWS. Knowledge of additional AI/ML frameworks and tools. Knowledge of Agentic AI * Experience with DevOps practices and CI/CD pipelines.