Must-Have Skills

∙Python Programming (Advanced)

∙Machine Learning (Scikit-Learn, XGBoost, LightGBM)

∙Deep Learning (Tensor Flow, PyTorch)

∙Generative AI and Prompt Engineering

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

∙Lang Chain / Lang Graph

∙RAG (Retrieval Augmented Generation)

∙Vector Databases (Pinecone, ChromaDB, Weaviate, FAISS)

∙REST API Development (FastAPI, Flask)

∙SQL and NoSQL Databases

∙Git, CI/CD and Dev Ops 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

∙Git Hub 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.

∙Robust 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, Tensor Flow, PyTorch, Scikit-Learn, GenAI, LLM,

OpenAI, Azure OpenAI, Gemini, Lang Chain, Lang Graph, RAG, Vector Database, MLOps,

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

📌 Python Fullstack + AI/ML (India)
🏢 GK HR Consulting India Private
📍 India