Job Description
Experience Required
5+ years
Job Description
About the Role
Seeking a Senior Data Scientist who is equally comfortable building predictive models and architecting intelligent systems with Generative AI. This role is ideal for someone who thrives on solving complex business problems with the power of data, machine learning, and cutting-edge AI techniques — including large language models (LLMs), RAG pipelines, prompt engineering and agentic AI.
You’ll be at the forefront of innovation, driving the design and deployment of scalable AI solutions that power intelligent products, enhance automation, and enable smarter decision‑making across the organization.
Key Responsibilities
- Develop & Deploy AI Solutions: Design, build, and optimize advanced machine learning pipelines for a variety of enterprise and research applications, leveraging best‑in‑class programming, frameworks, and cloud platforms.
- Implement, fine‑tune, and optimize generative AI models (e.g., LLMs, diffusion models, VAEs, transformers) for processing unstructured, multimodal, and structured data.
- Research Integration: Translate cutting‑edge academic research into practical, scalable AI solutions aligned with business objectives and innovation agenda.
- Model Evaluation & ML Ops: Support the end‑to‑end model lifecycle from ideation, training, and validation through deployment and monitoring, applying ML Ops best practices for operational excellence.
- Collaboration & Stakeholder Engagement: Work closely with data scientists, engineers, business stakeholders, and product teams to deliver robust, scalable, and ethical solutions.
- Documentation & Communication: Clearly communicate technical results, methodologies, and insights to both technical and non‑technical audiences; contribute to research publications and internal knowledge sharing.
- Continuous Learning: Stay current with the latest advancements in AI/ML, tools, and frameworks; actively contribute to a culture of innovation and excellence within the AI COE and identify opportunities for adoption.
Generative AI & LLMs
- Fine‑tune and apply large language models (LLMs) for summarization, classification, generation, and dialogue use cases.
- Design and implement retrieval‑augmented generation (RAG) pipelines using vector databases.
- Develop intelligent agents, agentic workflows and chat‑based interfaces that translate natural language into actions or insights.
- Uphold standards for ethical, equitable, and transparent AI, ensuring compliance with legal and security guidelines.
- Develop robust ML models for classification, regression, clustering, and time‑series forecasting.
- Apply advanced feature engineering, model evaluation, and explainability techniques.
- Collaborate with stakeholders to define data‑driven solutions aligned with business KPIs.
Deployment & MLOps
- Package and deploy ML/GenAI models into production using CI/CD pipelines, preferably on Azure or similar platforms.
- Monitor model performance, automate retraining, and ensure reliability through MLOps best practices.
Required Skills & Qualifications
- 5+ years of experience in data science and/or machine learning roles.
- Bachelor's, Master's degree, or Ph.D. in Computer Science, Data Science, Statistics, or a related field.
- Strong machine learning skills and 3+ years of experience productionizing machine learning models (Sklearn, XGBoost, or Deep Learning).
- Hands‑on experience with RAG systems, Gen AI‑related tech stack, including Langchain, HuggingFace, vector DBs, and agentic frameworks like LangGraph, Autogen.
- Strong programming skills in Python, with experience in SQL and cloud‑based data platforms (Azure/ AWS).
- Understanding of model governance, versioning, monitoring, and evaluation.
- Ability to translate business problems into analytical frameworks and actionable models.
- Strong problem‑solving mindset and ability to work in ambiguous, fast‑paced environments.
- Strong storytelling and stakeholder communication skills.
- Experience in fine‑tuning open‑source models like Llama etc. is a bonus.
- Experience with evaluation frameworks for generative AI, including human‑in‑the‑loop assessment is a bonus.
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