Agentic AI Engineer

Position Summary
We are seeking a highly experienced Senior AI Engineer with 8-10 years of overall IT experience, including a minimum of 5 years of experience building and deploying AI solutions and at least 3 years of hands-on experience designing and implementing Agentic AI solutions. The ideal candidate will have deep expertise in enterprise AI architectures, agent orchestration frameworks, Large Language Models (LLMs), vector databases, graph databases, and cloud-native AI platforms. Experience in the Banking, Financial Services, or FinTech domain is required.
Required Experience
8-10 years of overall IT experience.
Minimum 5 years of experience building enterprise AI/ML solutions.
Minimum 3 years of hands-on experience designing and deploying Agentic AI applications.
Strong experience in Banking, Financial Services, or FinTech.
Experience designing enterprise-grade AI platforms and production-ready AI agent ecosystems.
Required Technical Skills
Agentic AI Frameworks
Hands-on experience with one or more of:
LangChain
LangGraph
LangSmith
Google Agent Development Kit (ADK)
Microsoft AutoGen
CrewAI
Semantic Kernel
LlamaIndex
Large Language Models (LLMs)
Experience with enterprise LLM ecosystems including:
Google Gemini
OpenAI GPT
Anthropic Claude
Llama
Mistral
AI Engineering
Retrieval-Augmented Generation (RAG)
Multi-Agent Architecture
Agent Orchestration
Prompt Engineering
Tool Calling & Function Calling
Context Management
Embedding Models & Vectorization
Knowledge Graph Integration
LLM Evaluation & Observability
AI Governance & Responsible AI
Database Technologies
Relational Databases
Oracle
PostgreSQL
SQL Server
MySQL
NoSQL Databases
MongoDB
Cassandra
DynamoDB
Graph Databases (Preferred)
Neo4j
Amazon Neptune
TigerGraph
Vector Databases
Pinecone
Chroma
Vertex AI Vector Search
Elasticsearch Vector Search
Cloud Platforms
Experience with one or more:
Google Cloud Platform (Preferred) Vertex AI
Microsoft Azure Azure AI Services, Azure OpenAI
AWS Amazon Bedrock
Key Responsibilities
Design and develop enterprise-grade Agentic AI solutions for banking and financial services.
Build autonomous and multi-agent systems using modern AI orchestration frameworks.
Develop scalable RAG architectures leveraging vector databases and knowledge graphs.
Integrate LLMs with enterprise applications, APIs, and business workflows.
Implement monitoring, observability, and evaluation using tools such as LangSmith.
Collaborate with architects, engineering teams, and business stakeholders to identify AI-driven opportunities.
Establish AI engineering best practices, governance, and security controls.
Optimize AI agent performance, scalability, latency, and response quality.
Preferred Qualifications
Experience building AI assistants for banking operations, customer support, compliance, fraud detection, or risk management.
Experience with graph-based reasoning and knowledge graph architectures.
Knowledge of Model Context Protocol (MCP).
Familiarity with AI evaluation frameworks and LLMOps.
Experience with Kubernetes, Docker, CI/CD, and MLOps.
Understanding of enterprise security, regulatory, and compliance requirements within the banking industry.
Preferred Skills
Python
REST APIs
Git
Microservices
Kubernetes
Docker
CI/CD
MLOps
Enterprise AI Security