Ekloud - Lead Data Scientist - Generative AI

About The Role

We are seeking an accomplished and hands-on Lead Data Scientist to drive advanced AI initiatives, specifically within the realm of Agentic AI, Large Language Models (LLMs), and Generative AI (GenAI). This is a critical leadership position for a client-facing role that requires deep technical expertise, architectural foresight, and the ability to guide teams of LLM experts, GenAI engineers, and applied ML/DL practitioners.

The ideal candidate will not only possess a robust foundation in machine learning and deep learning, but also demonstrate strong proficiency in modern LLM ecosystems, prompt engineering, agentic workflows, cloud-native deployments, and production-grade MLOps practices.

Key Responsibilities

• Lead design, development, and deployment of LLM-augmented AI solutions and agentic frameworks for enterprise-grade applications.
• Architect fine-tuning, retrieval-augmented generation (RAG) pipelines, and multi-modal LLM integrations using embedding techniques and context-aware mechanisms.
• Define and drive end-to-end ML lifecycle, from data exploration and feature engineering to model development, validation, deployment, and monitoring.
• Conduct advanced exploratory data analysis (EDA), hypothesis testing, and statistical modeling to support data-driven decision making.
• Collaborate with cross-functional teams across engineering, DevOps, cloud architecture, and product to ensure scalable and efficient AI solutions.
• Lead code quality, maintainability, and modularity through clean architecture principles (Hexagonal Architecture, DDD, TDD).
• Guide the team in using AI-assisted development tools for enhanced productivity and intelligent automation.
• Present findings, insights, and progress updates to internal stakeholders and external clients with clarity and confidence.
  
  

Mandatory Technical Skills : & Frameworks :

Python (5+ years) : Strong command of Python for data science, including use of :

• Pandas, numpy, matplotlib, seaborn
• Code formatting and static analysis tools : pylint, black, flake8, mypy
  
  

Machine Learning & Deep Learning

Practical experience with :

• Supervised & Unsupervised Learning
• Scikit-learn, XGBoost, CatBoost, LightGBM
• Regression, Classification, Clustering, Dimensionality Reduction
• Model interpretability techniques (SHAP, LIME)
  
  

LLMs & Generative AI

Expertise in :

• Fine-tuning LLMs (e.g., GPT, LLaMA, Falcon, Mistral)
• RAG Pipelines, Embeddings, Multi-modal LLM architectures
• Prompt Engineering, Token Optimization, Context Window Management
• Understanding of Transformers, Attention Mechanisms, LoRA/PEFT
  
  

AI Developer Tools

Hands-on with :

• AI-assisted coding via VSCode integrations
• Advanced usage of Codium AI, Cursor, GitHub Copilot
• Integration of custom Copilot-like agents into dev pipelines
  
  

Cloud & DevOps

Experience deploying ML models on :

• AWS (SageMaker, EC2, Lambda)
• Google Cloud (Vertex AI)
• Azure ML Services
  
  

Containerization & Deployment

• Docker, Kubernetes, Terraform
• Serving models via REST APIs, gRPC, or FastAPI
  
  

Software Engineering Best Practices

Experience in :

• Hexagonal Architecture
• Domain-Driven Design (DDD)
• Test-Driven Development (TDD)
• CICD pipelines, GitOps practices
  
  

Version Control

Proficient with :

• Git, Bitbucket, Pull/Merge request workflows
  
  

Preferred Qualifications

• Prior experience with LangChain, Haystack, LlamaIndex, OpenAI APIs
• Exposure to multi-agent orchestration frameworks like Autogen, CrewAI, LangGraph
• Familiarity with VectorDBs (Pinecone, Weaviate, FAISS, Chroma)
• Understanding of MLOps tooling : MLflow, Weights & Biases, BentoML
  
  

(ref:hirist.tech)