AI Engineer

Tredence

Bengaluru

On-site

INR 4,000,000 - 7,000,000

Full time

14 days+

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Job summary

Tredence in Bengaluru seeks an AI Engineering Lead for AI Platform & Agent Systems. This hands-on leadership role owns end-to-end lifecycle of AI infrastructure, from design to deployment and optimization, across cloud and hybrid environments.

You'll guide architecture, mentor engineers, and shape production-grade AI solutions including LLM-based apps, RAG workflows, and vector data platforms, with a focus on reliability, security, and cost efficiency.

Qualifications

Strong experience building and deploying LLM-based applications and AI platforms.
Deep understanding of RAG architectures, vector databases, and AI inference pipelines.
Experience with self-hosted inference options and model APIs.

Responsibilities

Own architecture and development of AI platforms supporting agents, workflows, and LLM-based apps.
Lead engineering teams, establish standards, and ensure production-grade delivery.
Design scalable, secure cloud-native infrastructure across multi-environment deployments.

Skills

LLM-based apps

AI platforms

Kubernetes

GCP

Terraform

CI/CD

Security

Tools

GKE

ArgoCD

Weaviate/Pinecone

Job description

Role & responsibilites

AI Engineering Lead AI Platform & Agent Systems
Location: Bangalore, India
Experience: 6 -10 Years
Type: Full-Time
Role Overview
We are building a next-generation Enterprise AI Platform powering AI Agents, Multi-Agent Systems, RAG Applications, AI Workflows, and Knowledge Platforms.
We are looking for an AI Engineering Lead who will drive the design, architecture, and productionization of AI systems at scale. This is a hands-on leadership role responsible for owning the end-to-end lifecycle of AI infrastructure and applicationsfrom design and development to deployment, operations, and continuous optimization.
You will lead the AgentOps and AI Platform engineering efforts, ensuring that AI systems are scalable, reliable, secure, and enterprise-ready across cloud and hybrid environments.

Key Responsibilities

AI Platform & Architecture Leadership

Lead the architecture and development of AI platforms supporting agents, workflows, RAG systems, and LLM-based applications.
Define best practices for AI system design, model orchestration, inference pipelines, and runtime infrastructure.
Drive the evolution of AgentOps frameworks for managing AI agents at scale.

Engineering Leadership

Provide technical leadership and mentorship to a team of engineers working on AI infrastructure and platform systems.
Collaborate cross-functionally with AI Research, Product, and Platform teams to deliver production-grade AI solutions.
Establish engineering standards, design patterns, and development practices.

AI Systems & AgentOps

Design and manage AI agent architectures, workflow orchestration, and multi-agent systems.
Build and operate Model Gateways and LLM routing layers across providers (OpenAI, Azure OpenAI, Claude, Gemini, etc.).
Lead development of RAG systems with vector databases and retrieval pipelines.
Optimize latency, throughput, and cost of AI workloads.

Platform & Infrastructure Engineering

Own Kubernetes-based platforms for scalable AI workloads (GKE preferred).
Design and implement cloud-native architectures across GCP (primary), AWS/Azure (secondary).
Lead Infrastructure-as-Code (Terraform) and platform automation initiatives.
Establish CI/CD and GitOps-based deployment models for AI systems.

Reliability, Observability & Operations

Define and implement SRE practices including monitoring, alerting, and incident management.
Architect observability using OpenTelemetry, Prometheus, Grafana, ELK, or Cloud Monitoring.
Drive production readiness, scalability planning, and disaster recovery strategies.

Enterprise Deployments & Security

Ensure AI platform compliance with enterprise-grade security and governance standards.
Implement IAM, RBAC, SSO, secrets management, and network security controls.
Support customer deployments across cloud, hybrid, and on-prem environments.

Mandatory Skills & Experience

AI Engineering & Systems

Strong experience building and deploying LLM-based applications and AI platforms.
Deep understanding of RAG architectures, vector databases, and AI inference pipelines.
Experience with AI model APIs and/or self-hosted inference (vLLM, TGI, Ollama, etc.).
Knowledge of agent-based systems, workflow engines, and model orchestration patterns.

Cloud & Platform Engineering

Strong hands-on experience with Google Cloud Platform (GCP).
Expertise in services such as GKE, Cloud Run, BigQuery, Pub/Sub, IAM, VPC, and Monitoring.
Experience designing scalable, secure, multi-environment cloud architectures.

Kubernetes & Distributed Systems

Deep production experience with Kubernetes (GKE preferred).
Strong understanding of containerization, autoscaling, networking, storage, and high availability.
Experience with distributed systems and event-driven architectures.

Infrastructure & DevOps

Strong expertise in Terraform and Infrastructure-as-Code frameworks.
Experience with CI/CD, GitOps (ArgoCD, Flux), and release automation.
Proven ability to build automated, scalable platform infrastructure.

Observability & Reliability

Experience with monitoring, logging, and tracing systems (Prometheus, Grafana, OTEL, etc.).
Strong understanding of SRE principles, incident management, and reliability engineering.

Leadership & Ownership