Job Description

ManagerEXL/M/1466313


Digital SolutionsNoida
Posted On
01 Sep 2025
End Date
16 Oct 2025
Required Experience
5 - 10 Years



Basic Section
Number Of Positions


1


Band


C1


Band Name


Manager


Cost Code


G090628


Campus/Non Campus


NON CAMPUS


Employment Type


Permanent


Requisition Type


New


Max CTC


2000000.0000 - 2500000.0000


Complexity Level


-


Work Type


Hybrid - Working Partly From Home And Partly From Office


Organisational
Group


EXL Digital


Sub Group


Digital Solutions


Organization


Digital Solutions


LOB


Exelia-AI


SBU


CX CoE


Country


India


City


Noida


Center


Noida - Centre 59




Skills
Skill


PYTHON 3


JAVA


NODE JS


ML FRAMEWORKS, PYTORCH


AWS SERVICES


GCP


POSTGRE SQL


MONGODB


CI/CD PIPELINE


CI/CD INTEGRATION


SOC 2


Minimum Qualification


B TECH


Certification


No data available

: AI Developer

Job Summary

We are seeking a highly skilled and innovative

AI Developer

to design, build, and deploy artificial intelligence-driven solutions that enhance automation, decision-making, and user engagement across enterprise and consumer applications. The role involves working with

machine learning (ML), natural language processing (NLP), generative AI, and conversational AI

models while ensuring integration with business systems, cloud environments, and real-time applications.


The AI Developer will collaborate with cross-functional teams including solution architects, cloud engineers, data scientists, and business analysts to develop scalable AI solutions tailored to organizational needs. This role requires a combination of

software engineering expertise, AI/ML model development, and applied research

to create impactful and production-ready AI systems.

Key Responsibilities

1. AI/ML Model Development

Design, train, and fine-tune machine learning and deep learning models for tasks such as

classification, regression, clustering, recommendation, NLP, speech recognition, and generative AI

. Leverage

transformer-based architectures

(BERT, GPT, LLaMA, etc.) for conversational AI, summarization, Q&A, and semantic search. Implement

retrieval-augmented generation (RAG)

pipelines for contextual AI applications. Apply

reinforcement learning from human feedback (RLHF)

where appropriate.

2. Conversational AI & Generative AI Solutions

Build and maintain

intelligent virtual assistants, chatbots, and agent assist platforms

using frameworks like

Dialogflow CX, Rasa, Microsoft Bot Framework, and LangChain

. Integrate generative AI capabilities for

content creation, knowledge retrieval, summarization, and personalized responses

. Work with

speech-to-text (STT), text-to-speech (TTS)

, and multimodal AI systems to enable real-time conversational experiences.

3. Data Engineering & Preprocessing

Collect, clean, annotate, and transform datasets from

structured, unstructured, and streaming sources

. Use

feature engineering, embeddings, and vector databases (e.g., ChromaDB, Pinecone, Weaviate, FAISS)

for semantic search and contextual responses. Ensure compliance with

data privacy, PII redaction, and security standards (GDPR, SOC2, ISO 27001)

.

4. Software Engineering & Deployment

Develop AI-powered microservices in

Python, Node.js, or Java

, following best practices in modular design and CI/CD pipelines. Containerize and orchestrate deployments using

Docker, Kubernetes, and Helm charts

. Deploy models on

cloud platforms (GCP Vertex AI, AWS Sagemaker, Azure AI)

as well as on

edge devices

if required. Integrate APIs, webhooks, and real-time streaming protocols (

gRPC, WebSocket, REST

) for AI applications.

5. Collaboration & Research

Work closely with

solution architects, cloud architects, and QA teams

to align AI solutions with business goals. Stay updated with the latest AI research (transformers, multimodal AI, LLM fine-tuning, knowledge distillation). Publish technical documentation, design papers, and internal best practices. Mentor junior developers and contribute to organizational AI competency building.

Required Skills

Strong programming skills in

Python

(TensorFlow, PyTorch, Scikit-learn, Hugging Face). Knowledge of

machine learning pipelines, feature engineering, and model evaluation

. Experience with

conversational AI, NLP, and generative AI frameworks

. Proficiency in

SQL/NoSQL databases

and

vector databases

. Cloud experience:

AWS, Azure, or GCP

(AI/ML services, serverless computing, storage, and networking). Knowledge of

DevOps and MLOps tools

(GitHub Actions, Jenkins, MLflow, Kubeflow). Familiarity with

data security, compliance, and ethical AI principles

.

Preferred Skills

Experience with

multi-agent AI frameworks

and autonomous agents. Knowledge of

speech recognition, voice analytics, and emotion detection

. Practical understanding of

microservices architecture, API management

Hands-on with

Vector embeddings, RAG, and fine-tuning LLMs

. Exposure to

data annotation workflows

and quality assurance in AI datasets.

Tools & Technologies

Languages:

Python, Java, Node.js

Frameworks & Libraries:

TensorFlow, PyTorch, Hugging Face Transformers, LangChain, Rasa

Cloud Platforms:

AWS SageMaker, GCP Vertex AI, Azure AI

Databases:

PostgreSQL, MongoDB, Pinecone, FAISS, ChromaDB

CI/CD & MLOps:

Jenkins, GitHub Actions, MLflow, Docker, Kubernetes

Security & Compliance:

PII Redaction, SOC2, GDPR, TLS, Key Rotation

Example Projects

Conversational Agent Assist

- Built a real-time transcription and recommendation engine integrated with Five9/Genesis telephony and Dialogflow CX.

RAG-Powered Knowledge Bot

- Implemented a retrieval-augmented chatbot for enterprise knowledge management using ChromaDB and Hugging Face LLMs.

Document Digitization Pipeline

- Developed an autonomous AI pipeline for OCR, classification, and extraction of financial/legal documents.

Sentiment Analysis Engine

- Deployed a multi-lingual sentiment analysis system for customer support and escalation detection.

Career Path & Growth Opportunities

This role provides exposure to the

full lifecycle of AI solution development

--from research and prototyping to production-grade deployment. The AI Developer can progress to

Senior AI Engineer, AI Solution Architect, or Principal AI Scientist

, depending on specialization in

conversational AI, cloud-native AI infrastructure, or applied generative AI research

.



Workflow
Workflow Type


Digital Solution Center