Deputy Manager- AI Specialist

Position- Deputy Manager- AI Specialist

Job Type: Permanent on the payroll 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.

Age (in Years)

Min- 25

Max-35

Job Profile:

1. 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.

2. 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.

3. 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.

4. 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.

5. Infrastructure & Workload Security: Secure AI workloads on containers, Kubernetes, GPUs, and cloud-native platforms. Evaluate model hosting environments and serving infrastructure.

6. Governance, Reporting & Stakeholder Engagement: Present AI risks to senior management, participate in AI governance committees and architecture boards. Develop dashboards, support audits, and track emerging AI/ML threats.

7. Procurement, Supply Chain & Adoption: Support procurement/onboarding reviews for AI products. Review open-source AI components and supply chain risks. Establish secure GenAI usage guidelines for employees.

KRAs:

1. 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.

2. 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.

3. 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.

4. 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.

5. 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.

6. Monitoring, Incident Response & Stakeholder Engagement: Monitor AI apps for anomalies, abuse, and security incidents. Drive misuse/abuse detection, RCA, maintain AI asset inventory and risk register.

Support incident response/forensics, audits, and present risks to senior management. Build AI security awareness and collaborate with infra, data, legal, compliance, and architecture teams.

If interested on above profile, contact Sonali at +91 7738394880 / [email protected] or Swati at +91 8433957699/ [email protected].