Python Fullstack + AI/ML

Must-Have Skills

Python Programming (Advanced)

Machine Learning (Scikit-Learn, XGBoost, LightGBM)

Deep Learning (TensorFlow, PyTorch)

Generative AI and Prompt Engineering

Large Language Models (OpenAI, Azure OpenAI, Gemini, Claude, Llama)

LangChain / LangGraph

RAG (Retrieval Augmented Generation)

Vector Databases (Pinecone, ChromaDB, Weaviate, FAISS)

REST API Development (FastAPI, Flask)

SQL and NoSQL Databases

Git, CI/CD and DevOps Practices

AWS / Azure / GCP AI Services

Good-to-Have Skills

Agentic AI Frameworks

MLOps / LLMOps

Kubernetes & Docker

Apache Spark / Databricks

Knowledge Graphs

NLP and Computer Vision

GitHub Copilot

Data Engineering Pipelines

Key Responsibilities

Design, develop, and deploy AI/ML and Generative AI solutions for enterprise

applications.

Build and optimise machine learning models for prediction, recommendation,

classification, and automation use cases.

Develop GenAI applications using LLMs, RAG frameworks, vector databases, and AI

agents.

Create scalable APIs and microservices using FastAPI or Flask.

Implement MLOps practices for model deployment, monitoring, and lifecycle

management.

Fine-tune and evaluate LLMs and foundation models.

Collaborate with business stakeholders to convert AI use cases into production-ready

solutions.

Ensure AI solutions meet security, governance, and performance requirements.

Participate in architecture reviews, code reviews, and best-practice initiatives.

Required Qualifications

Bachelor's or Master's degree in Computer Science, Data Science, AI/ML, or related field.

5+ years of hands-on Python development experience.

2+ years of experience in AI/ML or Generative AI projects.

Strong understanding of ML algorithms, model evaluation, and data engineering

concepts.

Experience working with cloud AI services on AWS, Azure, or GCP.

Strong problem-solving and communication skills.

Preferred Experience

Developing enterprise AI assistants, copilots, chatbots, and GenAI applications.

Implementing RAG-based solutions using enterprise knowledge repositories.

Deploying ML models using MLOps tools and cloud platforms.

Working in Agile/Scrum environments.

TA Screening Keywords

Python, Machine Learning, Deep Learning, TensorFlow, PyTorch, Scikit-Learn, GenAI, LLM,

OpenAI, Azure OpenAI, Gemini, LangChain, LangGraph, RAG, Vector Database, MLOps,

LLMOps, FastAPI, Flask, AI Agent, Prompt Engineering, Databricks, Spark, NLP. .