We are looking for an innovative and proactive AI/ML Engineer with a deep specialization in Agentic AI and LLMs to drive the next generation of our intelligent automation and customer servicing platforms.

In this role, you will build sophisticated multi-model AI agents that enhance both customer and employee experiences, specifically within the voice and servicing domains.

If you are an expert in the Google Cloud stack, thrive on building autonomous agents using Vertex AI, and have hands-on experience with tools like n8n and Zapier to orchestrate complex workflows, we want you to lead our AI engineering efforts.

Key Responsibilities :

Agentic AI & LLM Development :

- Agent Architecture : Design and build autonomous Agentic AI systems using Google Cloud Vertex AI and LLMs (Gemini, OpenAI, etc.) capable of reasoning, planning, and executing complex tasks.

- Multi-Model Implementation : Implement and improve Multi-model Agentic AI solutions that seamlessly handle text, voice, and data for high-impact customer and employee-facing applications.

- Voice & Servicing AI : Develop specialized AI agents for voice interactions and servicing automation, ensuring natural, context-aware, and efficient user experiences.

Cloud AI & Workflow Automation :

- GCP Stack Mastery : Leverage the full power of the Google Cloud Platform (GCP) ecosystem, including BigQuery for data retrieval and Vertex AI for model training, tuning, and deployment.

- Workflow Orchestration : Utilize low-code/no-code automation tools like n8n and Zapier to integrate AI agents with various business applications, APIs, and data sources, creating end-to-end automated workflows.

- RAG Implementation : Build and optimize Retrieval-Augmented Generation (RAG) pipelines to ground AI responses in enterprise data, ensuring accuracy and relevance in financial contexts.

Model Deployment & Optimization :

- Production Deployment : Deploy robust AI/ML models and agents into production environments, ensuring low latency, high availability, and scalability.

- Continuous Improvement : Monitor agent performance, analyse interaction data, and iteratively improve model accuracy, conversation flow, and task success rates.

- Financial Services Modelling : Develop and fine-tune ML models specifically tailored for financial services use cases (e.g., fraud detection, personalized recommendations, risk assessment).

Must-Have Skills & Experience :

Experience :

- 4 - 7 years of professional experience in AI/ML Engineering, with a strong focus on Generative AI and LLMs in the last 2+ years.

- Proven track record of building and deploying Agentic AI or autonomous systems in a production environment.

Technical Proficiency :

- Core Languages : Expert proficiency in Python for AI development and scripting.

- Agentic AI & LLMs : Deep understanding of Large Language Models, prompt engineering, and agent frameworks (e.g., LangChain, AutoGen, Vertex AI Agents).

- Cloud AI Stack : Advanced expertise in Google Cloud Vertex AI (Agent Builder, Model Garden) and BigQuery.

- Automation Tools : Proficient in using workflow automation platforms like n8n and Zapier to connect AI agents with external systems.

- Voice AI : Experience with voice technologies (STT/TTS) and conversational AI platforms for implementing voice-enabled agents.

Domain Focus :

- Financial Services AI : Ability to build ML models and AI solutions specifically addressing the needs and regulations of the financial services industry.

Good-to-Have / Plus :

- Integration Expertise : DB and API integrations and ETL.

- DevOps for AI : Familiarity with CI/CD pipelines for AI models and containerization (Docker/Kubernetes).

- Certifications : Professional Machine Learning Engineer certification.

Education :

- B. Tech / B. / MCA / M. Sc or equivalent in Computer Science, Artificial Intelligence, or Data Science