Job Description -DataScientist I
Role Overview
We are seeking a highly motivatedDataScientist Iwith strong foundational knowledge in machine learning, modern AI techniques, and emerging Large Language Model (LLM) capabilities. The role requires handson experience with model development, finetuning, evaluation, and adherence to Responsible AI and regulatory guidelines (RBI/MeitY). You will collaborate with crossfunctional teams to build scalable, secure, and explainable AI systems that drive business value.
Key Responsibilities
1. Machine Learning & Statistical Modeling
- Develop and maintain ML models includingpropensity models, classification, regression, and clustering.
- Performdatacleaning, feature engineering, and exploratorydataanalysis.
- Build models using Python, SQL, and leading ML frameworks (TensorFlow, PyTorch, Scikitlearn).
2. Generative AI & LLMs
- Work withLarge Language Models (LLMs)andSmall Language Models (SLMs)for enterprise use cases.
- Applyfinetuning, distillation, and model optimizationtechniques to adapt models to business needs.
- Create and managesyntheticdatapipelinesfor training and evaluation.
3. AI Agents & Workflows
- Assist in designingAI agentsandagentic workflowsto automate decision-making processes.
- Contribute to buildingAI-driven orchestration systemsacross business workflows.
4. Model Evaluation & Guardrails
- ImplementLLM-as-a-Judge, evaluation frameworks, prompt tests, and model benchmarking.
- Applymodel risk assessment and mitigationstrategies as per enterprise AI governance.
- Implementsecurity guardrails, including DLP controls and content safety filters.
5. Responsible AI & Compliance
- Ensure all models comply with:
- RBI - Financial Regulation for Emerging Entities (FREE)guidelines
- MeitY AI &DataGovernance Guidelines
- IntegratePrivacy Preservation,Explainable AI (XAI), andResponsible AItechniques into model workflows.
6. Engineering & MLOps
- Participate inAIOps/MLOpsprocesses: model deployment, monitoring, versioning, CI/CD.
- Document experiments, track model performance, and support reproducible ML pipelines.
7.DataEngineering & Domain Collaboration
- Work with structured, unstructured, andgeospatialdatasets(a plus).
- Collaborate closely with product, engineering, analytics, and compliance teams to translate business problems into ML solutions.
Required Skills
- Strong proficiency inPython, ML libraries (scikitlearn, pandas, NumPy), and deep learning frameworks.
- Knowledge ofLLMs, SLMs, prompt engineering, and RAGconcepts.
- Familiarity withfine-tuning, quantization, pruning, and distillationmethods.
- Understanding ofmodel risks, adversarial ML, and mitigation strategies.
- Experience withAI/ML security, guardrails, and DLP principles.
- Understanding ofXAI tools(SHAP, LIME, Integrated Gradients).
- Sound knowledge ofResponsible AI, privacy techniques (DP, k-anonymity).
- Basic familiarity withAIOps/MLOps, Docker, Git, MLflow, Airflow (preferred).
- Exposure togeospatial analytics(nice to have).
Educational BackgroundJob Description -DataScientist I
Role Overview
We are seeking a highly motivatedDataScientist Iwith strong foundational knowledge in machine learning, modern AI techniques, and emerging Large Language Model (LLM) capabilities. The role requires handson experience with model development, finetuning, evaluation, and adherence to Responsible AI and regulatory guidelines (RBI/MeitY). You will collaborate with crossfunctional teams to build scalable, secure, and explainable AI systems that drive business value.
Key Responsibilities
1. Machine Learning & Statistical Modeling
- Develop and maintain ML models includingpropensity models, classification, regression, and clustering.
- Performdatacleaning, feature engineering, and exploratorydataanalysis.
- Build models using Python, SQL, and leading ML frameworks (TensorFlow, PyTorch, Scikitlearn).
2. Generative AI & LLMs
- Work withLarge Language Models (LLMs)andSmall Language Models (SLMs)for enterprise use cases.
- Applyfinetuning, distillation, and model optimizationtechniques to adapt models to business needs.
- Create and managesyntheticdatapipelinesfor training and evaluation.
3. AI Agents & Workflows
- Assist in designingAI agentsandagentic workflowsto automate decision-making processes.
- Contribute to buildingAI-driven orchestration systemsacross business workflows.
4. Model Evaluation & Guardrails
- ImplementLLM-as-a-Judge, evaluation frameworks, prompt tests, and model benchmarking.
- Applymodel risk assessment and mitigationstrategies as per enterprise AI governance.
- Implementsecurity guardrails, including DLP controls and content safety filters.
5. Responsible AI & Compliance
- Ensure all models comply with:
- RBI - Financial Regulation for Emerging Entities (FREE)guidelines
- MeitY AI &DataGovernance Guidelines
- IntegratePrivacy Preservation,Explainable AI (XAI), andResponsible AItechniques into model workflows.
6. Engineering & MLOps
- Participate inAIOps/MLOpsprocesses: model deployment, monitoring, versioning, CI/CD.
- Document experiments, track model performance, and support reproducible ML pipelines.
7.DataEngineering & Domain Collaboration
- Work with structured, unstructured, andgeospatialdatasets(a plus).
- Collaborate closely with product, engineering, analytics, and compliance teams to translate business problems into ML solutions.
Required Skills
- Strong proficiency inPython, ML libraries (scikitlearn, pandas, NumPy), and deep learning frameworks.
- Knowledge ofLLMs, SLMs, prompt engineering, and RAGconcepts.
- Familiarity withfine-tuning, quantization, pruning, and distillationmethods.
- Understanding ofmodel risks, adversarial ML, and mitigation strategies.
- Experience withAI/ML security, guardrails, and DLP principles.
- Understanding ofXAI tools(SHAP, LIME, Integrated Gradients).
- Sound knowledge ofResponsible AI, privacy techniques (DP, k-anonymity).
- Basic familiarity withAIOps/MLOps, Docker, Git, MLflow, Airflow (preferred).
- Exposure togeospatial analytics(nice to have).
Educational Background
- Bachelor's/Master's inComputerScience,DataScience, Mathematics, Statistics, or related fields.
Experience Required
- 1-2 yearsof hands-on experience in ML/AI projects, internships, research, or capstone projects.
Nice-to-Have
- Experience withLangChain, LlamaIndex, or other agent frameworks.
- Participation in AI/ML competitions (Kaggle, Hackathons).
- Knowledge of BFSI domain analytics (advantage but not mandatory).
- Bachelor's/Master's inComputerScience,DataScience, Mathematics, Statistics, or related fields.
Experience Required
- 1-2 yearsof hands-on experience in ML/AI projects, internships, research, or capstone projects.
Nice-to-Have
- Experience withLangChain, LlamaIndex, or other agent frameworks.
- Participation in AI/ML competitions (Kaggle, Hackathons).
- Knowledge of BFSI domain analytics (advantage but not mandatory).
