Senior AI Engineer

Jobtailor; Chennai District; On-site; INR 900,000 - 1,500,000; Full time

Job summary

Jobtailor in Chennai, India, is seeking an early-career data scientist/ML engineer to design and implement LLM-based solutions, including chatbots and document intelligence. You will build RAG pipelines, manage data ingestion and embeddings, and advance prompt-driven workflows, with hands-on experience across LangChain and cloud-native tech.

Role requires strong Python/PySpark skills, data analysis capabilities, and familiarity with AI/ML frameworks, plus collaboration across teams to ensure

Qualifications

• Bachelor’s or Master’s degree in Data Science, Computer Science, AI/ML, Statistics, Mathematics, or a related field.
• 0–4 years of experience in a data science, applied ML, or GenAI role, with a strong portfolio of projects.
• Exposure to AI/ML, NLP, or Data Engineering projects.
• Hands-on experience or strong learning exposure to LLM/GenAI use cases through projects, POCs, academic work, or professional work.
• Hands-on experience with LLMs such as Claude and OpenAI.
• Experience with RAG pipelines and retrieval optimisation.
• GPT and Agentic AI implementation experience.
• Experience with LangChain, LangGraph, or similar frameworks.
• Experience with agent orchestration and tool-calling architectures.
• Deep understanding of LLM limitations, evaluation, and optimisation strategies.
• Strong Python/PySpark engineering expertise with production-grade development and proven API integration experience.
• Deep data analysis experience and experience handling large volumes of data.
• Fabric, Azure Databricks, or Snowflake data engineering integration skills.
• Exposure to Azure, AWS, or GCP cloud platforms.
• Knowledge of SQL, containers, CI/CD, and monitoring.
• Hands-on experience with machine learning frameworks including scikit-learn, TensorFlow, and PyTorch.
• Practical experience with LLMs, GenAI frameworks, LangChain, and prompt-driven workflows.
• Good-to-have exposure to agentic workflows or tool-calling concepts.
• Basic knowledge of fine-tuning/prompt tuning, including LoRA and PEFT, is optional.
• Experience with Azure OpenAI, Azure AI Search, or similar stacks.
• Awareness of enterprise AI considerations such as data security, privacy, and governance.

Responsibilities

• Design and develop LLM-based solutions for business use cases, including chatbots, summarisation, and document intelligence.
• Build and optimise RAG pipelines covering data ingestion, embeddings, and retrieval.
• Implement prompt engineering techniques including prompt design, chaining, and optimisation.
• Develop backend services and APIs for AI applications using FastAPI, Flask, or Streamlit.
• Integrate LLM solutions with enterprise systems and structured/unstructured data sources.
• Apply guardrails and evaluation techniques to improve response quality and reduce hallucinations.
• Collaborate with cross-functional teams to ensure data quality, model performance, and deployment readiness.
• Document solutions and contribute to reusable components and best practices.

Skills

LLM Development

RAG Pipeline Optimization

Python Engineering

Data Analysis

API Integration

Machine Learning

NLP

Prompt Engineering

Data Engineering

SQL

Education

Bachelor’s or Master’s degree in Data Science, Computer Science, AI/ML, Statistics, Mathematics, or related field

Tools

FastAPI

Flask

Streamlit

LangChain

Azure Databricks

Snowflake

Azure

AWS

GCP

Scikit-learn

Job description

• Design and develop LLM-based solutions for business use cases, including chatbots, summarisation, and document intelligence
• Build and optimise RAG pipelines covering data ingestion, embeddings, and retrieval
• Implement prompt engineering techniques including prompt design, chaining, and optimisation
• Develop backend services and APIs for AI applications using FastAPI, Flask, or Streamlit
• Integrate LLM solutions with enterprise systems and structured/unstructured data sources
• Apply guardrails and evaluation techniques to improve response quality and reduce hallucinations
• Collaborate with cross-functional teams to ensure data quality, model performance, and deployment readiness
• Document solutions and contribute to reusable components and best practices

Requirements

• Bachelor’s or Master’s degree in Data Science, Computer Science, AI/ML, Statistics, Mathematics, or a related field
• 0–4 years of experience in a data science, applied ML, or GenAI role, with a strong portfolio of projects
• Exposure to AI/ML, NLP, or Data Engineering projects
• Hands-on experience or strong learning exposure to LLM/GenAI use cases through projects, POCs, academic work, or professional work
• Hands-on experience with LLMs such as Claude and OpenAI
• Experience with RAG pipelines and retrieval optimisation
• GPT and Agentic AI implementation experience
• Experience with LangChain, LangGraph, or similar frameworks
• Experience with agent orchestration and tool-calling architectures
• Deep understanding of LLM limitations, evaluation, and optimisation strategies
• Strong Python/PySpark engineering expertise with production-grade development and proven API integration experience
• Deep data analysis experience and experience handling large volumes of data
• Fabric, Azure Databricks, or Snowflake data engineering integration skills
• Exposure to Azure, AWS, or GCP cloud platforms
• Knowledge of SQL, containers, CI/CD, and monitoring
• Hands-on experience with machine learning frameworks including scikit-learn, TensorFlow, and PyTorch
• Practical experience with LLMs, GenAI frameworks, LangChain, and prompt-driven workflows
• Good-to-have exposure to agentic workflows or tool-calling concepts
• Basic knowledge of fine-tuning/prompt tuning, including LoRA and PEFT, is optional
• Experience with Azure OpenAI, Azure AI Search, or similar stacks
• Awareness of enterprise AI considerations such as data security, privacy, and governance

Demonstrates expertise in designing and developing LLM-based solutions, including chatbots and document intelligence, while optimizing RAG pipelines and implementing prompt engineering techniques. Proficient in backend service development and API integration for AI applications, with a strong focus on data quality and model performance.

Highest-signal resume keywords

• LLM Development
• RAG Pipeline Optimization
• FastAPI Development
• Python Engineering
• Data Analysis

ATS Optimization Keywords

Hard Skills

• LLM
• GenAI
• Prompt Engineering
• Data Ingestion
• Embeddings
• Retrieval Optimization
• API Integration
• Machine Learning
• Data Engineering
• SQL

Industry Keywords

• NLP
• Data Science
• AI/ML
• Data Security
• Privacy
• Governance
• Agentic AI
• Tool-Calling Architectures
• Evaluation Techniques
• Enterprise AI

Tools & Technologies

• FastAPI
• Flask
• Streamlit
• LangChain
• Azure Databricks
• Snowflake
• Azure
• AWS
• GCP
• Scikit-learn