Data Scientist – Agentic AI

Data Scientist – Agentic AI at VY SYSTEMS PRIVATE LIMITED · Bengaluru (Bangalore), Hyderabad · 5 - 8 years · ₹2L - ₹15L / yr · Profitable · Posted 7 Oct 2026

Data Scientist – Agentic AI

at VY SYSTEMS PRIVATE LIMITED

Posted by Vaishnavi E

5 - 8 yrs

₹2L - ₹15L / yr

Bengaluru (Bangalore), Hyderabad

Skills

Data Science

Python

Machine Learning (ML)

Statistics/ML Fundamentals

GenAI/LLM

Agentic AI

RAG

LangChain/LangGraph,

AI Tracing/Observability.

Model Validation/Evaluation

PySpark

BERT/LLaMA/Open-Source LLMs

LLM Fine-Tuning,

Role Overview

We are looking for an experienced Data Scientist – Agentic AI with strong expertise in Python, Machine Learning, Generative AI, Large Language Models (LLMs), RAG and Agentic AI.

The ideal candidate should have hands-on experience in developing, fine-tuning, evaluating and deploying machine learning and GenAI solutions. The candidate should be comfortable working with open-source LLMs, LangChain/LangGraph, PySpark and AI observability/tracing frameworks.

The role involves building intelligent AI systems that can reason, use tools, retrieve information and execute multi-step tasks using Agentic AI architectures.

Mandatory Technical Skills

1. Data Science & Python

5+ years of experience in Data Science / Machine Learning / AI.
Strong programming experience in Python.
Strong understanding of data analysis, feature engineering and statistical techniques.
Experience with Python ML and data science libraries such as:
NumPy
Pandas
Scikit-learn
Matplotlib / Seaborn
Good understanding of data preprocessing, exploratory data analysis and experimentation.

2. Machine Learning & Statistics

Strong understanding of Machine Learning fundamentals.
Experience with supervised and unsupervised learning techniques.
Knowledge of:
Regression
Classification
Clustering
Feature Engineering
Model Selection
Hyperparameter Tuning
Cross-validation
Strong understanding of Statistics / ML fundamentals.
Ability to interpret model performance and statistical results.

3. Generative AI / LLM

Strong hands-on experience with Generative AI and Large Language Models (LLMs).
Understanding of Transformer architecture and modern LLM-based applications.
Experience working with commercial or open-source LLMs.
Strong understanding of:
Prompt Engineering
Context Management
Embeddings
Tokenization
LLM inference
Hallucination mitigation

4. RAG – Retrieval Augmented Generation

Strong hands-on experience developing RAG applications.
Experience with:
Document ingestion
Chunking
Embeddings
Vector search
Semantic search
Retrieval pipelines
Context retrieval
Reranking
Ability to optimize RAG pipelines for relevance, accuracy and latency.
Experience integrating LLMs with enterprise knowledge sources.

5. Agentic AI

Hands-on experience building Agentic AI / AI Agent solutions.
Understanding of agent architecture and multi-step reasoning workflows.
Experience with:
AI Agents
Multi-Agent systems
Tool Calling
Function Calling
Agent orchestration
Planning and reasoning workflows
Memory
Workflow automation
Ability to build agents that can interact with tools, APIs, databases and external systems.

6. LangChain / LangGraph

Strong hands-on experience with LangChain and/or LangGraph.
Experience building LLM workflows and agent-based applications.
Understanding of:
Chains
Agents
Tools
State management
Graph-based workflows
Agent orchestration
Retrieval workflows
Experience designing scalable Agentic AI workflows.

7. LLM Fine-Tuning

Hands-on experience with LLM fine-tuning.
Understanding of techniques such as:
Supervised Fine-Tuning (SFT)
Parameter-Efficient Fine-Tuning
LoRA
QLoRA
Experience preparing datasets for fine-tuning.
Ability to evaluate fine-tuned models against baseline models.
Understanding of model optimization and inference considerations.

8. BERT / LLaMA / Open-Source LLMs

Experience working with one or more open-source / transformer-based models such as:

BERT
LLaMA / Llama
Mistral
Gemma
Qwen
Other open-source LLMs

Candidate should understand model loading, inference, fine-tuning and evaluation.

9. PySpark

Strong experience with PySpark for large-scale data processing.
Experience working with large datasets and distributed data processing.
Knowledge of:
Data transformations
Data cleaning
Aggregations
Joins
Spark SQL
Performance optimization
Ability to build scalable data processing pipelines.

10. Model Validation & Evaluation

Experience validating and evaluating ML and GenAI models.
Understanding of traditional ML evaluation metrics.
Experience evaluating LLM/RAG applications using relevant quality metrics.
Ability to compare model performance and identify areas for improvement.
Experience with:
Accuracy
Precision
Recall
F1 Score
ROC-AUC
Retrieval metrics
LLM response quality
Groundedness / relevance
Experience designing evaluation datasets and test cases is preferred.

11. AI Tracing / Observability

Experience with AI/LLM tracing and observability.
Ability to monitor AI applications in production.
Experience tracking:
LLM requests/responses
Latency
Token usage
Errors
Retrieval performance
Agent/tool execution
Model performance
Exposure to tools/frameworks such as LangSmith, OpenTelemetry, Arize Phoenix, MLflow or similar is preferred.

12. Model Deployment

Experience deploying ML/LLM/GenAI solutions into production.
Exposure to cloud and/or on-premise model deployment.
Experience with model serving, APIs and production inference.
Knowledge of deployment environments such as:
AWS
Azure
GCP
On-premise infrastructure
Experience with Docker, APIs and CI/CD is an advantage.

Key Responsibilities

Design, develop and deploy Data Science, Machine Learning and GenAI solutions.
Build production-ready RAG and Agentic AI applications.
Develop intelligent agents capable of tool calling, reasoning and multi-step task execution.
Build LLM-powered applications using LangChain/LangGraph.
Work with open-source LLMs including BERT, LLaMA and other transformer-based models.
Fine-tune LLMs for specific business use cases.
Develop scalable data processing pipelines using PySpark.
Perform data analysis, feature engineering and statistical modeling.
Develop and maintain model validation and evaluation frameworks.
Evaluate ML and LLM models using appropriate performance and quality metrics.
Implement AI tracing, monitoring and observability for production GenAI systems.
Deploy models and AI applications in cloud or on-premise environments.
Optimize model performance, response quality, latency and cost.
Troubleshoot issues related to model inference, retrieval, agents and LLM workflows.
Collaborate with Data Scientists, ML Engineers, Software Engineers and business stakeholders.
Convert business requirements into scalable AI/ML solutions.

Good to Have

Experience with Vector Databases such as:
FAISS
Pinecone
Weaviate
Milvus
Chroma
Azure AI Search
Experience with MLflow or similar ML lifecycle tools.
Experience with Docker/Kubernetes.
Experience with REST APIs / FastAPI.
Knowledge of cloud AI/ML services.
Experience with MLOps / LLMOps.
Experience with multi-agent frameworks other than LangChain/LangGraph.
Experience working with enterprise GenAI applications.

Ideal Candidate Profile

The ideal candidate should be a Data Scientist / ML Engineer with strong GenAI and Agentic AI experience, rather than a pure Python developer.

A strong candidate would typically have:

Data Science + Python + ML + Statistics + GenAI/LLM + RAG + Agentic AI + LangChain/LangGraph + LLM Fine-Tuning + Open-Source LLMs + PySpark + Model Evaluation + AI Observability + Model Deployment.

Core Mandatory Skills

Data Science, Python, Machine Learning, Statistics/ML Fundamentals, GenAI/LLM, RAG, Agentic AI, LangChain/LangGraph, LLM Fine-Tuning, BERT/LLaMA/Open-Source LLMs, PySpark, Model Validation/Evaluation, AI Tracing/Observability, Cloud/On-Prem Model Deployment.

Role Overview

We are looking for an experienced Data Scientist – Agentic AI with strong expertise in Python, Machine Learning, Generative AI, Large Language Models (LLMs), RAG and Agentic AI.

The role involves building intelligent AI systems that can reason, use tools, retrieve information and execute multi-step tasks using Agentic AI architectures.

Mandatory Technical Skills

1. Data Science & Python

5+ years of experience in Data Science / Machine Learning / AI.
Strong programming experience in Python.
Strong understanding of data analysis, feature engineering and statistical techniques.
Experience with Python ML and data science libraries such as:
NumPy
Pandas
Scikit-learn
Matplotlib / Seaborn
Good understanding of data preprocessing, exploratory data analysis and experimentation.

2. Machine Learning & Statistics

Strong understanding of Machine Learning fundamentals.
Experience with supervised and unsupervised learning techniques.
Knowledge of:
Regression
Classification
Clustering
Feature Engineering
Model Selection
Hyperparameter Tuning
Cross-validation
Strong understanding of Statistics / ML fundamentals.
Ability to interpret model performance and statistical results.

3. Generative AI / LLM

Strong hands-on experience with Generative AI and Large Language Models (LLMs).
Understanding of Transformer architecture and modern LLM-based applications.
Experience working with commercial or open-source LLMs.
Strong understanding of:
Prompt Engineering
Context Management
Embeddings
Tokenization
LLM inference
Hallucination mitigation

4. RAG – Retrieval Augmented Generation

Strong hands-on experience developing RAG applications.
Experience with:
Document ingestion
Chunking
Embeddings
Vector search
Semantic search
Retrieval pipelines
Context retrieval
Reranking
Ability to optimize RAG pipelines for relevance, accuracy and latency.
Experience integrating LLMs with enterprise knowledge sources.

5. Agentic AI

Hands-on experience building Agentic AI / AI Agent solutions.
Understanding of agent architecture and multi-step reasoning workflows.
Experience with:
AI Agents
Multi-Agent systems
Tool Calling
Function Calling
Agent orchestration
Planning and reasoning workflows
Memory
Workflow automation
Ability to build agents that can interact with tools, APIs, databases and external systems.

6. LangChain / LangGraph

Strong hands-on experience with LangChain and/or LangGraph.
Experience building LLM workflows and agent-based applications.
Understanding of:
Chains
Agents
Tools
State management
Graph-based workflows
Agent orchestration
Retrieval workflows
Experience designing scalable Agentic AI workflows.

7. LLM Fine-Tuning

Hands-on experience with LLM fine-tuning.
Understanding of techniques such as:
Supervised Fine-Tuning (SFT)
Parameter-Efficient Fine-Tuning
LoRA
QLoRA
Experience preparing datasets for fine-tuning.
Ability to evaluate fine-tuned models against baseline models.
Understanding of model optimization and inference considerations.

8. BERT / LLaMA / Open-Source LLMs

Experience working with one or more open-source / transformer-based models such as:

BERT
LLaMA / Llama
Mistral
Gemma
Qwen
Other open-source LLMs

Candidate should understand model loading, inference, fine-tuning and evaluation.

9. PySpark

Strong experience with PySpark for large-scale data processing.
Experience working with large datasets and distributed data processing.
Knowledge of:
Data transformations
Data cleaning
Aggregations
Joins
Spark SQL
Performance optimization
Ability to build scalable data processing pipelines.

10. Model Validation & Evaluation

Experience validating and evaluating ML and GenAI models.
Understanding of traditional ML evaluation metrics.
Experience evaluating LLM/RAG applications using relevant quality metrics.
Ability to compare model performance and identify areas for improvement.
Experience with:
Accuracy
Precision
Recall
F1 Score
ROC-AUC
Retrieval metrics
LLM response quality
Groundedness / relevance
Experience designing evaluation datasets and test cases is preferred.

11. AI Tracing / Observability

Experience with AI/LLM tracing and observability.
Ability to monitor AI applications in production.
Experience tracking:
LLM requests/responses
Latency
Token usage
Errors
Retrieval performance
Agent/tool execution
Model performance
Exposure to tools/frameworks such as LangSmith, OpenTelemetry, Arize Phoenix, MLflow or similar is preferred.

12. Model Deployment

Experience deploying ML/LLM/GenAI solutions into production.
Exposure to cloud and/or on-premise model deployment.
Experience with model serving, APIs and production inference.
Knowledge of deployment environments such as:
AWS
Azure
GCP
On-premise infrastructure
Experience with Docker, APIs and CI/CD is an advantage.

Key Responsibilities

Design, develop and deploy Data Science, Machine Learning and GenAI solutions.
Build production-ready RAG and Agentic AI applications.
Develop intelligent agents capable of tool calling, reasoning and multi-step task execution.
Build LLM-powered applications using LangChain/LangGraph.
Work with open-source LLMs including BERT, LLaMA and other transformer-based models.
Fine-tune LLMs for specific business use cases.
Develop scalable data processing pipelines using PySpark.
Perform data analysis, feature engineering and statistical modeling.
Develop and maintain model validation and evaluation frameworks.
Evaluate ML and LLM models using appropriate performance and quality metrics.
Implement AI tracing, monitoring and observability for production GenAI systems.
Deploy models and AI applications in cloud or on-premise environments.
Optimize model performance, response quality, latency and cost.
Troubleshoot issues related to model inference, retrieval, agents and LLM workflows.
Collaborate with Data Scientists, ML Engineers, Software Engineers and business stakeholders.
Convert business requirements into scalable AI/ML solutions.

Good to Have

Experience with Vector Databases such as:
FAISS
Pinecone
Weaviate
Milvus
Chroma
Azure AI Search
Experience with MLflow or similar ML lifecycle tools.
Experience with Docker/Kubernetes.
Experience with REST APIs / FastAPI.
Knowledge of cloud AI/ML services.
Experience with MLOps / LLMOps.
Experience with multi-agent frameworks other than LangChain/LangGraph.
Experience working with enterprise GenAI applications.

Ideal Candidate Profile

The ideal candidate should be a Data Scientist / ML Engineer with strong GenAI and Agentic AI experience, rather than a pure Python developer.

A strong candidate would typically have:

Core Mandatory Skills

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About VY SYSTEMS PRIVATE LIMITED

Founded :

2000

Type :

Services

Size :

100-1000

Stage :

Profitable

About

Vy Systems is a Global Technology consulting, Solutions, and Managed Technology Services company. We service our customers with ‘RESPONSIVENESS’ as a key factor and we believe that timely response to any transaction increases the operational efficiency and accelerates the revenue and profitability to our customers.

The Company is founded and managed by a team of professionals having more than two+ decades of global experience in the business of Technology Consulting and Services.

Tech stack

IT consulting

Company social profiles

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· Experience working with large and complex datasets.

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· Knowledge of AI security, data privacy, governance, and responsible AI.

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