Primary responsibilities

- Migrate the existing AI solution currently running on AWS, to the Google Gemini / Vertex AI platform.

- Integrate existing solutions with with RMB's document management and other enterprise system.

- Design, build, and deploy additional AI use cases and agents on the Gemini Enterprise Agent Platform, in collaboration with business and technology stakeholders.

- Work closely with customer's internal tech team, operations team, business team, and the central team.

- Participate in stakeholder engagements to assess use case feasibility, scope requirements, and translate business needs into technical solutions.

- Provide hands-on technical guidance on Gemini stack architecture, agent design patterns, and AI implementation best practices.

- Adhere to enterprise AI guardrails, implementation guidelines, and compliance requirements throughout the build and delivery.

Required technical skills

Google Cloud / Gemini AI stack (mandatory)

- Hands-on experience with the Gemini Enterprise Agent Platform (formerly Vertex AI) including Agent Studio, Model Garden, and Vertex AI Studio.

- Proficiency with the Agent Development Kit (ADK) for code-first agent development.

- Experience with Vertex AI pipelines, model fine-tuning, and prompt engineering.

- Strong working knowledge of BigQuery, AlloyDB, or Vertex AI Search for RAG-based architectures.

AI / ML engineering

- Experience designing and deploying production-grade AI agents and LLM-based applications.

- Solid understanding of RAG (Retrieval-Augmented Generation) architectures and vector databases.

- Familiarity with multi-agent orchestration patterns and agentic workflows.





- Experience with cloud migration — specifically AWS to Google Cloud transitions is a strong advantage.

- Knowledge of document processing pipelines and integration with document management and other enterprise systems.

Software engineering

- Proficiency in Python; familiarity with JavaScript/TypeScript is advantageous.

- Experience with REST API design, microservices, and cloud-native application patterns.

- Familiarity with CI/CD pipelines, containerization (Docker/Kubernetes), and IaC (Terraform).

- Ability to read and contribute to enterprise codebases and solution architecture documentation.

Experience and qualifications

- 5+ years of overall software and data engineering experience, with at least 3 years in AI/ML engineering.

- Demonstrated experience delivering AI solutions in a regulated industry — banking, financial services, or insurance strongly preferred.

- Prior experience working in a client-facing or consulting engagement model.

- Bachelor's degree or higher in Computer Science, Engineering, or a related technical field.

- Good to have, Google Cloud certifications (Cloud Architect, Professional ML Engineer) are a significant advantage.

Soft skills and ways of working

- Strong stakeholder communication, engagement and trust building skills — ability to explain technical concepts clearly to non-technical business audiences.

- Self-directed and comfortable operating with ambiguity in a fast-moving AI workplace.

- Collaborative team player who can work across business, operations, and technology functions.

- High attention to compliance, data governance, and security requirements — essential for a banking context.

- Ability to guide and upskill junior team members as part of the engagement.

📌 Nihilent is Hiring For Senior AI Engineer (Pune)
🏢 Nihilent
📍 Pune