Ekloud - Lead Data Scientist - Generative AI (5-8 yrs)

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 :

Programming & 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

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