Description

Key Skills: Pandas, AI Engineering, Numpy, Python, LLM, Retrieval-Augmented Generation (RAG)

Roles and Responsibilities:

AI Solution Architecture & Engineering

•  
  • Lead the end-to-end architecture, design, development, and implementation of scalable AI-powered applications and services.
  • Build production-ready AI solutions using Python, FastAPI/Flask, LangGraph, ADK, FastMCP, and Google Vertex AI.
  • Design robust AI architectures that are scalable, secure, maintainable, and aligned with enterprise standards.
  • Integrate AI capabilities into enterprise applications and business processes.

LLM & Generative AI

•  
  • Architect, integrate, optimize, and deploy Large Language Models (LLMs), with a strong focus on Google Gemini.
  • Develop advanced Generative AI and Natural Language Processing (NLP) solutions.
  • Design and implement Retrieval-Augmented Generation (RAG) solutions.
  • Evaluate model performance and implement appropriate optimization and deployment strategies.
  • Develop solutions leveraging prompt engineering, model integration, grounding, and contextual retrieval.

Agentic AI & Conversational AI

•  
  • Design and implement advanced Agentic AI architectures and autonomous AI workflows.
  • Develop multi-agent and workflow-based solutions using LangGraph and ADK.
  • Orchestrate complex AI agents and workflows for enterprise use cases.
  • Develop and optimize conversational AI solutions using platforms such as Dialogflow CX.
  • Establish reusable patterns and frameworks for enterprise Agentic AI applications.

API & System Integration

•  
  • Design and implement scalable APIs and integration architectures.
  • Develop enterprise-grade APIs using FastAPI or Flask.
  • Integrate AI services with internal and external enterprise systems.
  • Ensure reliable data flow, security, scalability, and interoperability across systems.
  • Define API design standards and integration best practices.

GCP & Cloud Infrastructure

•  
  • Lead the deployment and optimization of AI/ML solutions on Google Cloud Platform (GCP).
  • Work extensively with:
    • Google Vertex AI
    • Cloud Run
    • GKE
    • BigQuery
    • Google Cloud Storage (GCS)
    • Cloud SQL
    • Cloud Composer
  • Design cloud architectures supporting scalable AI workloads.
  • Ensure reliability, performance, security, and cost optimization of cloud-based AI solutions.
  • Familiarity with Terraform / Infrastructure as Code (IaC) is an advantage.

AI Operations & Quality

•  
  • Establish and enforce coding standards, development best practices, and AI engineering governance.
  • Define comprehensive testing strategies for AI applications and services.
  • Lead deployment, monitoring, and optimization of AI models in production.
  • Apply AIOps/MLOps practices to ensure reliability, scalability, and operational excellence.
  • Monitor AI application performance and continuously improve production systems.
  • Ensure responsible, secure, and ethical use of AI and data.

Innovation & Technical Leadership

•  
  • Research emerging developments in AI, Machine Learning, Generative AI, LLMs, and NLP.
  • Evaluate new technologies and frameworks and translate relevant innovations into practical enterprise solutions.
  • Provide technical leadership and mentorship to AI engineers and development teams.
  • Establish technical standards and reusable AI engineering patterns.
  • Drive continuous improvement and innovation across AI initiatives.

Stakeholder & Cross-Functional Collaboration

•  
  • Partner with product managers, data scientists, engineers, architects, and business stakeholders.
  • Translate complex business requirements into scalable AI solutions.
  • Lead technical discussions and influence architecture and technology decisions.
  • Communicate complex AI concepts clearly to both technical and non-technical stakeholders.
  • Take ownership of strategic AI initiatives and ensure successful delivery.

Skills Required:

Mandatory Technical Skills

•  
  • Python - Expert level.
  • AI Engineering / Machine Learning.
  • Large Language Models (LLMs).
  • Generative AI.
  • Retrieval-Augmented Generation (RAG).
  • Google Vertex AI - strong hands-on experience.
  • Google Gemini models.
  • LangGraph.
  • ADK (Agent Development Kit).
  • FastAPI / Flask.
  • Pandas and NumPy.
  • SQL.
  • Agentic AI architecture and orchestration.
  • API design and enterprise integration.
  • Strong experience with GCP.

GCP Skills

Strong hands-on experience with:

•  
  • Google Vertex AI
  • Cloud Run
  • GKE
  • BigQuery
  • Google Cloud Storage (GCS)
  • Cloud SQL
  • Cloud Composer

Desirable Skills

•  
  • Terraform / Infrastructure as Code.
  • Dialogflow CX.
  • FastMCP.
  • scikit-learn.
  • AIOps / MLOps.
  • Svelte.
  • Angular.
  • Django.
  • Streamlit.
  • Taipy.
  • Experience with enterprise AI governance and responsible AI practices.

Experience Requirements

•  
  • 13+ years of overall experience, with significant experience in AI Engineering.
  • Proven experience designing and delivering enterprise-scale AI solutions.
  • Strong experience with LLM-based applications and Generative AI.
  • Demonstrated experience building and deploying AI solutions in production.
  • Strong experience with Agentic AI and AI workflow orchestration.
  • Proven experience with Google Cloud and Vertex AI.
  • Experience leading technical initiatives and mentoring engineering teams.
  • Strong understanding of software engineering principles, architecture, testing, deployment, and production support.

Domain Experience

Experience in the Financial Services / Banking / Investment Banking domain is highly desirable.

Candidates with experience delivering AI solutions for financial services, risk, finance, treasury, operations, or other regulated environments will be preferred.

Education:

•  
  • Bachelor's, Master's, or PhD in a relevant quantitative or technical discipline such as:
    • Computer Science
    • Artificial Intelligence / Machine Learning
    • Data Science
    • Statistics
    • Mathematics
    • Engineering

Key Competencies

•  
  • Exceptional analytical and problem-solving skills.
  • Strong strategic and architectural thinking.
  • Excellent communication and stakeholder management skills.
  • Ability to lead cross-functional technical teams.
  • Strong decision-making and influencing skills.
  • Passion for innovation and continuous learning

Key Skills

Education

Any Graduate