Data Scientist

About the Role

Straive is looking for a talented and driven Consultant / Data Scientist / GenAI Engineer to join our Analytics & GenAI delivery team . In this role, you will work under the guidance of the Senior Project Manager / Engagement Manager to design, develop, and deploy advanced AI / ML and Generative AI solutions for global enterprise clients. You will be part of a high-performing team, collaborating with both onshore and offshore members to build scalable, production-grade AI systems.

This role is ideal for candidates from premier engineering institutes with 2–3 years of relevant experience in Python development, LLM integration, and RAG workflows, along with a passion for solving complex problems in real-world business contexts.

Key Responsibilities

Develop and maintain Python-based applications, AI / ML models, and data processing pipelines for GenAI projects.

Implement Large Language Model (LLM) integrations, including Retrieval-Augmented Generation (RAG) pipelines and embedding-based search solutions.

Build data ingestion and transformation workflows, working with structured and unstructured datasets.

Optimize AI model performance through prompt engineering , fine-tuning, and evaluation techniques.

Collaborate closely with senior team members to translate business requirements into technical solutions.

Integrate AI solutions with vector databases (e.g., Cosmos DB, Pinecone, ChromaDB) and API-driven applications.

(Optional) Contribute to cloud-native deployments and Azure architecture –based solutions, including containerization, CI / CD, and basic MLOps workflows.

Document workflows, maintain code repositories, and follow Agile development practices.

Required Qualifications

2–3 years of relevant experience in AI / ML development, preferably in enterprise projects.

Bachelor’s or Master’s degree in Computer Science, Data Science, AI / ML, or related field from a premier engineering institute .

Proficiency in Python programming and familiarity with relevant libraries (e.g., LangChain, Hugging Face, Pandas, NumPy).

Hands-on experience implementing RAG pipelines , embeddings, and vector search solutions.

Understanding of LLM architectures and integration patterns.

Working knowledge of SQL and data processing best practices.

Basic knowledge of cloud DevOps concepts , preferably with Azure (AWS / GCP experience is also acceptable).

Preferred Skills

Exposure to agentic AI frameworks such as LangGraph, Semantic Kernel, or similar.

Familiarity with ML model lifecycle management and deployment workflows.

Prior experience working with cross-border teams and Agile environments.