Deputy Manager- AI Specialist

Position- Deputy Manager (AI Specialist) at SBI Bank Job Type: Permanent on the payrolls of SBI Bank Location- Mumbai BASIC QUALIFICATION (As on 31.07.2026) Mandatory: B. Tech/ B.E. in Computer Science/ Computer Science & Engineering/ Data Science & Artificial Intelligence/ Software Engineering/ Information Technology/ Electronics/ Electronics & Communications Engineering or Equivalent Degree in above specified disciplines with minimum 50% score or MCA or M. Tech/ M. Sc in Computer Science/ Computer Science & Engineering/ Information Technology/ Data Science & Artificial Intelligence/ Software Engineering/ Electronics/ Electronics & Communications Engineering or Equivalent Degree in above specified disciplines from a University/ Institution/ Board recognized by Govt. Of India/ approved by Govt. Regulatory Bodies. Experience (As on 31.07.2026) 4 years post qualification experience in Information Technology domain. Out of 4 years, 2 years experience in Designing ML/Deep Learning Models /mapping workflows/ deploying GenAI/RAG pipelines / Secure API Deployment / Experience with cloud ML suits like AWS SageMaker, Google Cloud Vertex AI or Microsoft Azure ML/ Expertise in libraries like TensorFlow, PyTorch, Scikit-Learn, Pandas/ XGBoost/ Hugging Face/ Lang Chain/ Open AI API / Apache Spark/Python/ Prompt Engineering/ Fine Tuning. Responsibilities: AI/ML Development & Integration: Support integration testing of AI/analytics models, dashboards, PoCs, and autonomous tools like LLM agents. Train SLMs, GANs, etc., and prep datasets for workflow automation.AI Security Controls & Governance: Define security controls for AI dev, testing, deployment, and ops. Establish model integrity, authenticity, versioning, provenance controls, and KRIs/KPIs. Map controls to enterprise frameworks and ensure regulatory compliance.Risk & Threat Assessment: Assess risks of foundation models, open source models, 3rd-party AI services, and hosting/inference infra. Conduct AI red teaming, adversarial testing, and RCA for AI security incidents.Data Privacy & Protection: Secure sensitive/regulated data in AI systems. Evaluate anonymization, tokenization, masking, and privacy enhancing tech. Review AI solutions for privacy/confidentiality risks.Infrastructure & Workload Security: Secure AI workloads on containers, Kubernetes, GPUs, and cloud-native platforms. Evaluate model hosting environments and serving infrastructure. KRAs: AI/ML Development & MLOps: Train, fine-tune, and deploy SLMs, LLMs, CNNs, GANs for workflow automation. Build RAG pipelines, embeddings, and data repositories. Operationalize models using CI/CD, versioning, monitoring, retraining, and optimize for performance/cost.Agentic Systems & Integration: Design autonomous LLM agents/harnesses that orchestrate end-to-end workflows. Support integration testing of AI models, dashboards, PoCs, and ensure compatibility with existing systems, APIs, plugins, and 3rd-party integrations.AI Security Governance & Frameworks: Develop and implement organization-wide AI/LLM security governance framework and secure development standards. Establish controls for model integrity, authenticity, versioning, provenance across dev, testing, deployment, and ops.Risk Assessment & Threat Modeling: Assess risks for foundation models, open-source models, 3rd-party AI services, and workloads on containers/Kubernetes/GPUs/cloud. Perform threat modeling for prompt injection, jailbreak, model poisoning, data leakage. Conduct adversarial testing and red teaming.Data Privacy, Compliance & Guardrails: Safeguard sensitive/regulated data via anonymization, tokenization, masking, PHE tech. Secure RAG pipelines, vector DBs, and knowledge repos. Implement AI guardrails, content filtering, misuse prevention. Ensure alignment with RBI, CERT-In, DPDP Act, ISO 42001, NIST AI RMF, and other regulations. For more details, contact Swati at: hr.21@tnmhr.com +91 8433957699 .