Skills

Must have

8-12 years of professional software engineering experience, including meaningful experience building production AI/LLM-powered products, not just prototypes or POCs.
Proven expertise building agentic AI applications, multi-agent systems and tool-calling using orchestration frameworks such as LangGraph, AutoGen, CrewAI, or a comparable custom stack.
Strong full-stack proficiency in both frontend (React, Angular, or similar) and backend (Node.js, Python, Java, or similar) technologies.
Hands-on experience with RAG, prompt engineering, embeddings/vector search, and evaluation frameworks.
Hands-on experience with REST APIs, microservices, and cloud platforms (Azure, AWS, or GCP).
A genuine, demonstrated inclination to experiment hands-on with the newest models, frameworks, and AI techniques as they emerge, rather than waiting for them to become mainstream.
Demonstrated leadership, formal or informal: you've mentored engineers, led a small project team, or driven technical direction, and are ready to formally lead a small team as it scales.
Strong problem-solving skills and the ability to translate business requirements into technical solutions.
Excellent communication skills, with the ability to provide thought leadership and influence both engineers and business stakeholders.

8-12 years of professional software engineering experience, including meaningful experience building production AI/LLM-powered products, not just prototypes or POCs.
Proven expertise building agentic AI applications, multi-agent systems and tool-calling using orchestration frameworks such as LangGraph, AutoGen, CrewAI, or a comparable custom stack.
Strong full-stack proficiency in both frontend (React, Angular, or similar) and backend (Node.js, Python, Java, or similar) technologies.
Hands-on experience with RAG, prompt engineering, embeddings/vector search, and evaluation frameworks.
Hands-on experience with REST APIs, microservices, and cloud platforms (Azure, AWS, or GCP).
A genuine, demonstrated inclination to experiment hands-on with the newest models, frameworks, and AI techniques as they emerge, rather than waiting for them to become mainstream.
Demonstrated leadership, formal or informal: you've mentored engineers, led a small project team, or driven technical direction, and are ready to formally lead a small team as it scales.
Strong problem-solving skills and the ability to translate business requirements into technical solutions.
Excellent communication skills, with the ability to provide thought leadership and influence both engineers and business stakeholders.

Lead AI Product Engineer - Full Stack

Compensation

Not disclosed

Location

Bangalore

Work setup

Not specified

Seniority

Lead

Employment

Not specified

Experience

8+ years

8-12 years of professional software engineering experience, including meaningful experience building production AI/LLM-powered products, not just prototypes or POCs.
Proven expertise building agentic AI applications, multi-agent systems and tool-calling using orchestration frameworks such as LangGraph, AutoGen, CrewAI, or a comparable custom stack.
Strong full-stack proficiency in both frontend (React, Angular, or similar) and backend (Node.js, Python, Java, or similar) technologies.
Hands-on experience with RAG, prompt engineering, embeddings/vector search, and evaluation frameworks.
Hands-on experience with REST APIs, microservices, and cloud platforms (Azure, AWS, or GCP).
A genuine, demonstrated inclination to experiment hands-on with the newest models, frameworks, and AI techniques as they emerge, rather than waiting for them to become mainstream.
Demonstrated leadership, formal or informal: you've mentored engineers, led a small project team, or driven technical direction, and are ready to formally lead a small team as it scales.
Strong problem-solving skills and the ability to translate business requirements into technical solutions.
Excellent communication skills, with the ability to provide thought leadership and influence both engineers and business stakeholders.

8-12 years of professional software engineering experience, including meaningful experience building production AI/LLM-powered products, not just prototypes or POCs.
Proven expertise building agentic AI applications, multi-agent systems and tool-calling using orchestration frameworks such as LangGraph, AutoGen, CrewAI, or a comparable custom stack.
Strong full-stack proficiency in both frontend (React, Angular, or similar) and backend (Node.js, Python, Java, or similar) technologies.
Hands-on experience with RAG, prompt engineering, embeddings/vector search, and evaluation frameworks.
Hands-on experience with REST APIs, microservices, and cloud platforms (Azure, AWS, or GCP).
A genuine, demonstrated inclination to experiment hands-on with the newest models, frameworks, and AI techniques as they emerge, rather than waiting for them to become mainstream.
Demonstrated leadership, formal or informal: you've mentored engineers, led a small project team, or driven technical direction, and are ready to formally lead a small team as it scales.
Strong problem-solving skills and the ability to translate business requirements into technical solutions.
Excellent communication skills, with the ability to provide thought leadership and influence both engineers and business stakeholders.