Senior GenAI Engineer

TechNET; India; Remote; INR 3,600,000 - 7,200,000; Part time

Job summary

TechNET is seeking a Senior GenAI Lead to design and drive enterprise Agentic AI architecture in a fully remote, India-based contract role. You will own the architecture, guide RAG pipelines, and optimize costs while ensuring security and governance.

The role requires hands-on expertise with Generative AI, LLMs, and production AI operations, plus strong leadership to mentor engineers and coordinate distributed teams across functions.

Qualifications

• 5+ years in Generative AI and enterprise AI development.
• Hands-on with LLMs, RAG, and tool calling.
• Experience leading architecture and cross-functional teams.
• Strong focus on observability, cost optimization, and governance.

Responsibilities

• Own AI architecture and roadmap for Agentic AI solutions.
• Improve AI agents, RAG pipelines, prompts, and guardrails.
• Optimize latency, scalability, and cost efficiency.
• Collaborate with AI, data, app dev, QA, and business teams.
• Lead reviews, estimations, and post-production issue analysis.
• Establish AI metrics, observability, and KPIs.
• Mentor AI Engineers, Data Engineers, and App Developers.
• Drive architecture quality and knowledge sharing.
• Coordinate distributed teams and onboarding.

Skills

Generative AI

LLMs

RAG pipelines

Prompt engineering

Production ops

AI security

Leadership

Mentoring

Job description

Senior GenAI | Agentic AI | RAG | LLM Architecture - Contract

Location: Remote, India

Duration: 6+ Months

Positions: 1

Engagement: Contract

About the Role

We are seeking an experienced Technical Lead - Senior Generative AI to drive the architecture, development, and continuous improvement of enterprise Agentic AI solutions. The ideal candidate will have strong hands-on expertise in Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), prompt engineering, and production AI operations, along with proven technical leadership skills.

Key Responsibilities

AI Architecture and Solution Leadership

• Own the technical architecture, design decisions, and roadmap for Agentic AI solutions.
• Continuously improve AI agents, RAG pipelines, prompts, guardrails, and model performance.
• Optimise solution quality, accuracy, latency, scalability, and cost efficiency.
• Evaluate AI/ML approaches and recommend solutions for new business use cases.

Technical Delivery and Production Operations

• Translate business requirements into scalable, secure, and maintainable AI solution designs.
• Collaborate with AI, data, application development, QA, and business teams.
• Lead technical reviews, effort estimations, impact assessments, and root-cause analysis of production issues.
• Establish AI evaluation metrics, observability, monitoring, and performance KPIs.
• Optimise LLM usage, token consumption, inference costs, and retrieval mechanisms.
• Ensure security, governance, compliance, and Responsible AI practices.
• Lead, mentor, and guide AI Engineers, Data Engineers, and Application Developers.
• Drive best practices in architecture, code quality, documentation, and knowledge sharing.
• Coordinate distributed teams, resource planning, and onboarding.

Stakeholder Management

• Present technical recommendations, architectural decisions, and roadmaps to business stakeholders.
• Support architecture reviews, governance discussions, and production readiness assessments.
• Communicate delivery progress, risks, dependencies, and milestones.

Required Skills and Experience

• Strong hands-on experience in Generative AI, LLMs, and enterprise AI application development.
• Expertise in Agentic AI, AI agent design, orchestration, and tool calling.
• Experience designing and implementing production-grade RAG applications.
• Strong knowledge of prompt engineering, embeddings, vector search, retrieval optimisation, and LLM guardrails.
• Experience with scalable AI architecture, LLM integration, and production deployment.
• Understanding of model evaluation, AI observability, monitoring, latency, and cost optimisation.
• Experience with AI security, Responsible AI, governance, and compliance.
• Proven technical leadership, mentoring, architecture review, and stakeholder management experience.