AI Data Analyst

Dwc; Bengaluru; On-site; INR 1,800,000 - 3,000,000; Full time

Job summary

Dwc in Bengaluru, India seeks a talented ML engineer to design, build, and deploy LLM-based applications, including RAG pipelines and fine-tuned models. You will expose AI capabilities via REST APIs using Flask and own projects end-to-end from prototyping to monitoring.

Required: strong Python, ML fundamentals, extensive LLM experience, RAG systems, and familiarity with LangChain, Docker, and CI/CD workflows. Collaboration with product teams is essential.

Qualifications

• Strong proficiency in Python, including data manipulation, numerical computing, and software design patterns.
• Solid foundations in Data Science and ML — statistics, model evaluation, feature engineering, experiment design.
• Hands-on experience with LLMs (GPT, Claude, Mistral, Llama, etc.) including prompt engineering, context management, and output evaluation.
• Practical experience building RAG systems — chunking strategies, vector stores, retrieval tuning, and grounding.
• Experience with LLM fine-tuning techniques (instruction tuning, LoRA, PEFT) and understanding of when to fine-tune vs. prompt.
• Familiarity with agentic AI patterns — tool-use, memory, orchestration frameworks (LangChain, LlamaIndex, AutoGen, etc.)
• Experience building and deploying REST APIs with Flask or similar frameworks
• Proficiency with CI/CD tools (GitHub Actions, GitLab CI, or similar) and deployment workflows
• Comfort with cloud platforms (AWS or Azure) for model hosting, storage, and serving
• Familiarity with containerization (Docker) and basic MLOps practices

Responsibilities

• Design, build, and deploy LLM-based applications including RAG pipelines, fine-tuned models, and multi-step agentic workflows
• Develop and expose AI capabilities as clean APIs using Flask or equivalent frameworks
• Own problems end-to-end — from scoping and prototyping to production deployment and monitoring
• Set up and maintain CI/CD pipelines to ensure reliable, repeatable model and service deployments
• Conduct applied research to evaluate new models, architectures, and techniques relevant to business needs
• Collaborate with product and business stakeholders to translate requirements into robust AI solutions
• Write clean, well-tested, and maintainable code that your teammates can build on

Skills

Python

Data Science & ML

LLMs

RAG systems

Fine-tuning (LoRA/PEFT)

Agentic AI patterns

REST APIs

CI/CD

Cloud platforms

Containerization

Tools

Flask

LangChain

LlamaIndex

AutoGen

Docker

GitHub Actions

GitLab CI

AWS

Azure

Job description

• Design, build, and deploy LLM-based applications including RAG pipelines, fine-tuned models, and multi-step agentic workflows
• Develop and expose AI capabilities as clean APIs using Flask or equivalent frameworks
• Own problems end-to-end — from scoping and prototyping to production deployment and monitoring
• Set up and maintain CI/CD pipelines to ensure reliable, repeatable model and service deployments
• Conduct applied research to evaluate new models, architectures, and techniques relevant to business needs
• Collaborate with product and business stakeholders to translate requirements into robust AI solutions
• Write clean, well-tested, and maintainable code that your teammates can build on

Skill Requirements

Core Technical Skills

• Strong proficiency in Python, including data manipulation, numerical computing, and software design patterns
• Solid foundations in Data Science and ML — statistics, model evaluation, feature engineering, experiment design
• Hands-on experience with LLMs (GPT, Claude, Mistral, Llama, etc.) including prompt engineering, context management, and output evaluation
• Practical experience building RAG systems — chunking strategies, vector stores, retrieval tuning, and grounding
• Experience with LLM fine-tuning techniques (instruction tuning, LoRA, PEFT) and understanding of when to fine-tune vs. prompt
• Familiarity with agentic AI patterns — tool-use, memory, orchestration frameworks (LangChain, LlamaIndex, AutoGen, etc.)
• Experience building and deploying REST APIs with Flask or similar frameworks
• Proficiency with CI/CD tools (GitHub Actions, GitLab CI, or similar) and deployment workflows
• Comfort with cloud platforms (AWS or Azure) for model hosting, storage, and serving
• Familiarity with containerization (Docker) and basic MLOps practices