Technology Architect
Project Role :Technology Architect
Project Role Description :Design and deliver technology architecture for a platform, product, or engagement. Define solutions to meet performance, capability, and scalability needs.
Must have skills :Generative AI
Good to have skills :Python (Programming Language)
Minimum 5 year(s) of experience is required
Educational Qualification :15 years full time education
Summary:
We are seeking an experienced Full Stack Engineering Tech Lead with strong expertise in Generative AI, Agentic AI frameworks, and modern cloud-native application architectures.
The role involves leading the design and development of enterprise-scale AI platforms, including RAG pipelines, agentic AI workflows, and microservices-based backend systems. The candidate will work closely with product owners, architects, and engineering teams to build scalable AI-driven applications deployed on cloud platforms like AWS or GCP.
The ideal candidate should combine deep hands-on engineering expertise with technical leadership, driving innovation and mentoring engineers while building production-grade AI systems.
Role:Full Stack Engineering Tech Lead (GenAI / Agentic AI Platforms)
Roles & Responsibilities:
Technical Leadership
Lead the design, architecture, and development of AI-driven full stack applications.
Provide technical direction and best practices for implementing Generative AI and Agentic AI solutions.
Mentor and guide full stack engineers and AI engineers.
Conduct architecture reviews, code reviews, and technical design discussions.
Drive engineering excellence, scalability, and security standards across AI applications.
AI / GenAI Platform Development
Architect and implement enterprise RAG (Retrieval Augmented Generation) pipelines.
Design agentic AI workflows and orchestration pipelines using LangGraph.
Develop scalable LLM inferencing pipelines using open-source or enterprise LLMs.
Design document ingestion pipelines including chunking, embedding, vectorization, and retrieval workflows.
Evaluate and experiment with emerging AI standards and technologies such as MCP (Model Context Protocol).
Design multi-agent orchestration frameworks for enterprise AI applications.
Backend & Microservices Architecture
Design and develop scalable microservices architecture using NestJS and Python.
Define standards for REST API design, versioning, and secure API development.
Implement event-driven integrations using webhooks and asynchronous communication patterns.
Design DAG-based workflows and orchestration pipelines for AI processing pipelines.
Data & AI Infrastructure
Architect vector database solutions for semantic search and AI retrieval systems using:
opgVector
oOpenSearch
oMilvus
Design metadata and operational data storage architecture using PostgreSQL / AWS RDS.
Implement distributed data processing pipelines using Apache Spark or equivalent frameworks.
Optimize embedding storage, indexing, and retrieval performance.
Cloud & Platform Engineering
Architect and deploy AI applications on AWS or GCP cloud platforms.
Design cloud-native AI architectures leveraging containerized workloads and managed services.
Define scalability, reliability, and resilience strategies for AI platforms.
Collaborate with DevOps teams to implement CI/CD pipelines for AI application deployments.
Security & Compliance
Implement secure coding practices for APIs and AI applications.
Ensure compliance with OWASP API security standards.
Design secure AI pipelines ensuring data protection and access control.
Establish security guardrails for AI integrations and data pipelines.
Architecture & Design
Translate functional business requirements into technical architecture and system design.
Design RAG architecture, retrieval strategies, and agentic AI workflows.
Develop architecture diagrams, solution blueprints, and system documentation using draw.io.
Define design patterns for AI orchestration, microservices, and cloud-native deployments.
Professional & Technical Skills:
AI & GenAI Technologies
LangChain
LangGraph
RAG pipeline architecture
Agentic AI orchestration
LLM inferencing pipelines
Knowledge of emerging AI protocols such as MCP
Programming Languages
Python
NestJS / Node.js
Databases
Vector Databases
pgVector
OpenSearch
Milvus
Metadata Stores
PostgreSQL
AWS RDS
APIs & Integration
REST API development
Webhooks
Secure API design
OWASP API security practices
Data Processing
DAG-based processing pipelines
Apache Spark or distributed data processing frameworks
Cloud Platforms
AWS or GCP
Tools
draw.io
Git-based development
CI/CD tools
Desired Skills
Experience with LLMOps / MLOps practices
Knowledge of prompt engineering and LLM evaluation
Experience with semantic search and embedding optimization
Exposure to multi-agent AI architectures
Experience building enterprise knowledge assistants or AI copilots
Mandatory Project Experience
Candidate must have hands-on implementation experience in enterprise AI systems, including at least one of the following:
RAG-based enterprise knowledge assistant
LLM inferencing platform
Agentic AI workflow using LangChain / LangGraph
Document ingestion and semantic search pipeline
Experience may come from industry projects, research initiatives, or AI platform development programs.
Leadership & Soft Skills
Strong technical architecture skills
Ability to mentor engineers and guide development teams
Hands on technical design and programming
Strong problem-solving and analytical thinking
Excellent communication and stakeholder management skills
Ability to translate functional requirements into scalable technical architectures
Strong innovation mindset and experimentation with emerging AI technologies
What We Value
We value leaders who:
Drive innovation in Generative AI and modern engineering practices
Encourage experimentation and rapid prototyping
Build scalable and secure enterprise AI platforms
Foster continuous learning and technical excellence within engineering teams
610 Years of exp.
Additional Information:
- The candidate should have minimum 5 years of experience in Generative AI.
- This position is based at our Mumbai office.
- A 15 years full time education is required.
Qualification15 years full time education
Project Role Description :Design and deliver technology architecture for a platform, product, or engagement. Define solutions to meet performance, capability, and scalability needs.
Must have skills :Generative AI
Good to have skills :Python (Programming Language)
Minimum 5 year(s) of experience is required
Educational Qualification :15 years full time education
Summary:
We are seeking an experienced Full Stack Engineering Tech Lead with strong expertise in Generative AI, Agentic AI frameworks, and modern cloud-native application architectures.
The role involves leading the design and development of enterprise-scale AI platforms, including RAG pipelines, agentic AI workflows, and microservices-based backend systems. The candidate will work closely with product owners, architects, and engineering teams to build scalable AI-driven applications deployed on cloud platforms like AWS or GCP.
The ideal candidate should combine deep hands-on engineering expertise with technical leadership, driving innovation and mentoring engineers while building production-grade AI systems.
Role:Full Stack Engineering Tech Lead (GenAI / Agentic AI Platforms)
Roles & Responsibilities:
Technical Leadership
Lead the design, architecture, and development of AI-driven full stack applications.
Provide technical direction and best practices for implementing Generative AI and Agentic AI solutions.
Mentor and guide full stack engineers and AI engineers.
Conduct architecture reviews, code reviews, and technical design discussions.
Drive engineering excellence, scalability, and security standards across AI applications.
AI / GenAI Platform Development
Architect and implement enterprise RAG (Retrieval Augmented Generation) pipelines.
Design agentic AI workflows and orchestration pipelines using LangGraph.
Develop scalable LLM inferencing pipelines using open-source or enterprise LLMs.
Design document ingestion pipelines including chunking, embedding, vectorization, and retrieval workflows.
Evaluate and experiment with emerging AI standards and technologies such as MCP (Model Context Protocol).
Design multi-agent orchestration frameworks for enterprise AI applications.
Backend & Microservices Architecture
Design and develop scalable microservices architecture using NestJS and Python.
Define standards for REST API design, versioning, and secure API development.
Implement event-driven integrations using webhooks and asynchronous communication patterns.
Design DAG-based workflows and orchestration pipelines for AI processing pipelines.
Data & AI Infrastructure
Architect vector database solutions for semantic search and AI retrieval systems using:
opgVector
oOpenSearch
oMilvus
Design metadata and operational data storage architecture using PostgreSQL / AWS RDS.
Implement distributed data processing pipelines using Apache Spark or equivalent frameworks.
Optimize embedding storage, indexing, and retrieval performance.
Cloud & Platform Engineering
Architect and deploy AI applications on AWS or GCP cloud platforms.
Design cloud-native AI architectures leveraging containerized workloads and managed services.
Define scalability, reliability, and resilience strategies for AI platforms.
Collaborate with DevOps teams to implement CI/CD pipelines for AI application deployments.
Security & Compliance
Implement secure coding practices for APIs and AI applications.
Ensure compliance with OWASP API security standards.
Design secure AI pipelines ensuring data protection and access control.
Establish security guardrails for AI integrations and data pipelines.
Architecture & Design
Translate functional business requirements into technical architecture and system design.
Design RAG architecture, retrieval strategies, and agentic AI workflows.
Develop architecture diagrams, solution blueprints, and system documentation using draw.io.
Define design patterns for AI orchestration, microservices, and cloud-native deployments.
Professional & Technical Skills:
AI & GenAI Technologies
LangChain
LangGraph
RAG pipeline architecture
Agentic AI orchestration
LLM inferencing pipelines
Knowledge of emerging AI protocols such as MCP
Programming Languages
Python
NestJS / Node.js
Databases
Vector Databases
pgVector
OpenSearch
Milvus
Metadata Stores
PostgreSQL
AWS RDS
APIs & Integration
REST API development
Webhooks
Secure API design
OWASP API security practices
Data Processing
DAG-based processing pipelines
Apache Spark or distributed data processing frameworks
Cloud Platforms
AWS or GCP
Tools
draw.io
Git-based development
CI/CD tools
Desired Skills
Experience with LLMOps / MLOps practices
Knowledge of prompt engineering and LLM evaluation
Experience with semantic search and embedding optimization
Exposure to multi-agent AI architectures
Experience building enterprise knowledge assistants or AI copilots
Mandatory Project Experience
Candidate must have hands-on implementation experience in enterprise AI systems, including at least one of the following:
RAG-based enterprise knowledge assistant
LLM inferencing platform
Agentic AI workflow using LangChain / LangGraph
Document ingestion and semantic search pipeline
Experience may come from industry projects, research initiatives, or AI platform development programs.
Leadership & Soft Skills
Strong technical architecture skills
Ability to mentor engineers and guide development teams
Hands on technical design and programming
Strong problem-solving and analytical thinking
Excellent communication and stakeholder management skills
Ability to translate functional requirements into scalable technical architectures
Strong innovation mindset and experimentation with emerging AI technologies
What We Value
We value leaders who:
Drive innovation in Generative AI and modern engineering practices
Encourage experimentation and rapid prototyping
Build scalable and secure enterprise AI platforms
Foster continuous learning and technical excellence within engineering teams
610 Years of exp.
Additional Information:
- The candidate should have minimum 5 years of experience in Generative AI.
- This position is based at our Mumbai office.
- A 15 years full time education is required.
Qualification15 years full time education
