AI Architect(Gen AI & Agentic AI) 7 to 8yrs

Job Description: AI Architect – Generative AI \& Agentic AI

Experience: 7–8 Years

Industry: Banking / Financial Services preferred

Location: Mumbai(Thane /BKC) Work from Office

Role Type: Architecture, Engineering \& Technology Leadership

1\. About the Role

We are seeking a highly skilled AI Architect with 7–8 years of experience in designing, developing, and implementing enterprise\-grade AI solutions. The ideal candidate will combine strong software engineering fundamentals with deep expertise in Generative AI, Large Language Models (LLMs), Retrieval\-Augmented Generation (RAG), AI agents, and cloud\-native architectures.

The candidate will be responsible for translating business requirements into scalable, secure, resilient, and production\-ready AI solutions, with a focus on banking and financial services use cases. The role requires hands\-on technical leadership, architectural decision\-making, rapid prototyping, and collaboration with engineering, data, security, and business teams.

2\. Key Responsibilities

AI Architecture \& Solution Design

\* Define end\-to\-end architecture for enterprise AI, GenAI, and agentic AI solutions aligned with business objectives and enterprise architecture standards.

\* Design scalable AI platforms integrating LLMs, enterprise data sources, APIs, microservices, and cloud\-native infrastructure.

\* Evaluate and select appropriate foundation models, embedding models, orchestration frameworks, vector databases, and inference strategies.

\* Establish reusable AI architecture patterns, reference implementations, design principles, and engineering standards.

\* Design for high availability, scalability, low latency, cost optimisation, observability, and resilience.

Generative AI \& Agentic AI Engineering

\* Architect and implement RAG pipelines, including document ingestion, chunking, embedding generation, retrieval, reranking, and contextual response generation.

\* Design intelligent AI agents capable of tool calling, multi\-step reasoning, workflow orchestration, and controlled execution of enterprise business processes.

\* Develop multi\-agent workflows where appropriate, with clear boundaries, state management, human approval, and failure\-handling mechanisms.

\* Implement prompt engineering, structured outputs, model routing, context management, and conversation memory.

\* Apply guardrails to mitigate hallucinations, prompt injection, sensitive\-data exposure, and unauthorised tool execution.

\* Build proof of concepts and convert successful prototypes into production\-ready solutions.

Enterprise Integration \& Banking Use Cases

\* Integrate AI capabilities with enterprise applications, REST APIs, event\-driven systems, microservices, and secure data platforms.

\* Design AI solutions for banking use cases such as customer service automation, knowledge assistants, document intelligence, regulatory compliance, fraud investigation support, and developer productivity.

\* Ensure AI services integrate seamlessly with existing authentication, authorisation, audit, and monitoring frameworks.

\* Work with domain architects and business stakeholders to identify high\-value AI opportunities and translate them into technical solutions.

MLOps, LLMOps \& Production Engineering

\* Establish CI/CD pipelines and automated deployment practices for AI applications and model\-serving components.

\* Implement model evaluation, regression testing, prompt/version management, retrieval\-quality assessment, and continuous monitoring.

\* Define evaluation frameworks covering factual accuracy, groundedness, relevance, latency, safety, and token consumption.

\* Monitor model performance, drift, failure rates, inference costs, and application reliability.

\* Optimise inference performance through caching, batching, model selection, quantisation, and other suitable techniques.

Security, Governance \& Responsible AI

\* Design AI solutions with security and privacy by design, including encryption, role\-based access control, secrets management, and data minimisation.

\* Implement responsible AI controls, explainability where appropriate, traceability, audit logging, and human\-in\-the\-loop approvals.

\* Address threats such as prompt injection, data poisoning, insecure output handling, excessive agent permissions, and sensitive information leakage.

\* Collaborate with information security, risk, compliance, and data governance teams to meet applicable regulatory and organisational requirements.

Technical Leadership \& Collaboration

\* Provide hands\-on technical leadership to developers and engineering teams throughout the solution lifecycle.

\* Conduct architecture reviews, code reviews, technical design sessions, and proof\-of\-concept evaluations.

\* Mentor engineers on AI engineering best practices, design patterns, and production\-readiness standards.

\* Communicate architecture decisions, trade\-offs, risks, and implementation roadmaps to technical and business stakeholders.

3\. Required Technical Skills

Generative AI \& LLMs

\* Strong understanding of LLMs, transformer\-based models, embeddings, tokenisation, context windows, and inference.

\* Hands\-on experience with commercial and/or open\-source models such as GPT, Claude, Gemini, Llama, or equivalent.

\* Expertise in RAG, semantic search, hybrid retrieval, reranking, prompt engineering, and structured generation.

\* Practical experience with agent frameworks such as LangGraph, LangChain, Semantic Kernel, or equivalent.

Programming \& Backend Engineering

\* Strong Python programming skills and experience building production\-grade AI applications.

\* Solid understanding of REST APIs, asynchronous processing, API security, and distributed systems.

\* Experience with FastAPI, Flask, or equivalent backend frameworks.

\* Working knowledge of Java/Spring Boot and microservices is an advantage.

Data \& Search Technologies

\* Experience with vector databases or vector search technologies such as pgvector, Azure AI Search, Pinecone, Weaviate, or Milvus.

\* Strong understanding of SQL/NoSQL databases, document processing, data pipelines, and data quality.

\* Familiarity with knowledge graphs, hybrid search, and enterprise data integration is desirable.

Cloud \& Platform Engineering

\* Hands\-on experience with at least one major cloud platform: Microsoft Azure, AWS, or Google Cloud.

\* Experience deploying AI workloads using Docker, Kubernetes, and container\-based architectures.

\* Familiarity with API gateways, event\-driven architecture, secrets management, monitoring, and distributed tracing.

\* Experience with cloud AI platforms such as Azure AI Foundry, Amazon Bedrock, Vertex AI, or equivalent.

AI Quality, Evaluation \& Operations

\* Experience with LLM evaluation frameworks, automated testing, tracing, and observability tools.

\* Understanding of hallucination mitigation, grounding, model safety, and retrieval\-quality optimisation.

\* Knowledge of CI/CD, infrastructure as code, and MLOps/LLMOps practices.

4\. Qualifications \& Experience

\* 7–8 years of overall experience in software engineering, AI/ML engineering, solution architecture, or related technology roles.

\* Demonstrable hands\-on experience delivering AI or GenAI solutions beyond proof\-of\-concept stage.

\* Strong architectural problem\-solving skills and ability to make informed build\-versus\-buy and model\-selection decisions.

\* Experience working with enterprise\-scale applications, sensitive data, and production deployment constraints.

\* Bachelor’s or master’s degree in Computer Science, Information Technology, Engineering, or a related field.

\* Banking, financial services, insurance, or other regulated\-industry experience is highly desirable.

5\. Preferred Experience

\* Experience building agentic AI systems that invoke enterprise tools and APIs under controlled permissions.

\* Knowledge of AI security testing, red teaming, model risk management, and responsible AI practices.

\* Experience with multimodal AI, document intelligence, speech, or vision\-based solutions.

\* Familiarity with Kafka or other event\-streaming platforms and integration with microservices.

\* Experience designing AI solutions for high\-volume, low\-latency, business\-critical environments.

6\. Success Criteria

The AI Architect will be measured on the ability to

\* Deliver secure, scalable, production\-ready AI solutions aligned with business outcomes.

\* Establish reusable AI architecture patterns that accelerate delivery across engineering teams.

\* Improve answer quality, retrieval accuracy, reliability, and user adoption.

\* Optimise inference cost, response latency, and infrastructure utilisation.

\* Demonstrate measurable business value through automation, productivity improvements, and reduced manual effort.

\* Ensure AI solutions meet defined security, governance, auditability, and operational\-readiness requirements.

Ideal Candidate Profile: A hands\-on AI architect who can move from business problem definition to solution architecture, working code, production deployment, and measurable business impact—not just develop GenAI proofs of concept.

Pay: ₹1,500,000\.00 \- ₹2,500,000\.00 per year

Experience

• Agentic AI \& gen AI: 5 years (Required)

Work Location: In person