Sr. Data Scientist

Sr Full Stack Data Scientist Reporting Structure Project Manager Education · Bachelor’s Degree in Engineering Experience 6 years in applied ML/AI/Gen AI lead roles with demonstrable production deployments; must have led at least one complete production implementation . Mandatory experience with on‑prem LLMs and model hosting , including fine‑tuning and inference optimization. Proven expertise with Vector Databases and embedding pipelines for retrieval augmented generation and semantic search. Strong statistical foundations and experience designing experiments and evaluation frameworks. Proficiency in Python , PyTorch or TensorFlow, and model serving frameworks. Familiarity with containerization and orchestration for on‑prem environments. Proven experience and knowledge on LLM fine‑tuning, embeddings, RAG, transformers. Experience on Vector DBs: Elastic, Milvus, etc. and on indexing and similarity tuning. MLOps: model registries, CI/CD, automated testing, monitoring and drift detection. Data engineering: ETL, feature stores, data quality and lineage tools. Security: data encryption, IAM, audit logging, and compliance controls. Experience with model explainability tools, quantization, and inference acceleration. Familiarity with OpenShift or enterprise Kubernetes for on‑prem deployments. Banking domain experience with knowledge of regulatory, privacy, and risk constraints. Industry Preferably from a Technology, ITES, Products, Professional SI organization with a focus on BFSI. Responsibilities Lead end‑to‑end delivery of Generative AI and machine learning solutions with a measurable impact on business outcomes. This role requires hands‑on ownership of at least one complete production implementation , deep focus on model metrics and performance, and mandatory experience running LLMs and models on on‑prem infrastructure. Expertise with Vector Databases for retrieval and RAG is required. Banking domain experience is highly desirable. Key Responsibilities are as follows : · Own full AI/ML lifecycle of ML & Gen AI projects: Problem definition, data collection, feature engineering, model development, validation, deployment, and monitoring. Deliver at least one production implementation of an AI, ML or Gen AI application with actual implementation experience and knowledge of various production grade application being used by End users Define and track model metrics : accuracy, precision/recall, AUC, calibration, latency, throughput, business KPIs, and translate them into SLOs/SLA targets. Design and operate on‑prem LLMs : fine‑tuning, prompt engineering, RAG architectures, embedding generation, and secure inference. Integrate and manage Vector Databases : design embedding pipelines, select indexing strategies, tune similarity metrics, implement secure access controls, and monitor vector DB performance and cost. Implement robust validation and governance : bias and fairness checks, explainability, model risk assessments, drift detection, and automated retraining triggers. Productionize models with CI/CD, model registries, canary testing, and automated validation gates. Collaborate with stakeholders across product, platform, security, and compliance to align models with business needs and regulatory constraints. Mentor peers and produce reproducible notebooks, evaluation suites, and documentation. Certifications Data Science, Data Analytics, Big Data Certifications are preferred Location Mumbai / Navi Mumbai -