Lead AI Engineer

The Ksquare Group; Hyderabad; On-site; INR 3,500,000 - 6,000,000; Full time

Job summary

The Ksquare Group is seeking an experienced platform engineer to design and build the AI platform core, manage model lifecycle pipelines, and optimize serving across multiple providers. You will establish observability, developer tooling, and robust data retrieval for cutting‑edge AI products.

Collaborating with product and ML teams, you will implement guardrails, control cost, and drive production readiness in a fast‑moving environment based in Hyderabad, India.

Qualifications

• Strong software engineering fundamentals for distributed systems and production reliability.
• Hands-on experience building RAG pipelines or model serving infra.
• Deep understanding of LLM internals: tokenization, context windows, embeddings.
• Experience with at least one vector database (pgvector, Pinecone, Weaviate, Qdrant).
• Production observability/monitoring for ML/AI systems.
• Proficiency in Python; familiarity with Go/Rust/TypeScript for platform services.

Responsibilities

• Design and build the AI platform core, including routing, prompts, and embeddings at scale.
• Own model lifecycle infrastructure with fine-tuning and A/B testing.
• Build low-latency serving paths and caching/batching strategies.
• Establish observability: tracing, token/cost tracking, drift detection.
• Create internal developer tooling: SDKs and APIs to consume AI capabilities.
• Develop data and retrieval infrastructure: chunking, embeddings, hybrid search.
• Implement guardrails: content filters, PII redaction, abuse detection.
• Optimize cost and performance across teams with model selection and caching.

Skills

Distributed systems

APIs

Production reliability

RAG pipelines

Model serving infra

Fine-tuning pipelines

LLM orchestration

Vector databases

Python

Cloud/Infra (AWS)

Tools

Docker

Kubernetes

AWS

GCP

Azure

Job description

The Ksquare Group is a consulting company focused on delivering cutting‑edge technological solutions through customized projects that enhance decision‑making with speed and quality. By partnering with leading technology platforms, the company accelerates digital transformation initiatives for its clients. Its core expertise includes Salesforce and data solutions, helping organizations modernize processes and leverage insights for better business outcomes. Professionals joining The Ksquare Group can expect to work in a dynamic environment centered on innovation, collaboration, and high‑impact technology delivery.

Role Summary

• Design and build the AI platform core: model routing/gateway layer, prompt/version management, RAG pipelines, embedding infrastructure, and vector store operations at scale.
• Own model lifecycle infrastructure: fine‑tuning pipelines, evaluation harnesses, A/B testing frameworks for model/prompt changes, and regression detection when models or prompts change.
• Build serving infrastructure: low‑latency inference paths, caching layers, batching strategies, fallback/routing logic across multiple model providers (OpenAI, Anthropic, open‑source/self‑hosted).
• Establish observability for AI systems: tracing, token/cost tracking, hallucination/quality monitoring, drift detection — the stuff that’s invisible until it breaks in prod.
• Create internal developer tooling: SDKs, APIs, and abstractions so product teams can consume AI capabilities without reinventing infra each time.
• Data and retrieval infrastructure: chunking strategies, embedding pipelines, hybrid search, freshness/index management for RAG systems.
• Guardrails and safety infra: content filtering, PII redaction, rate limiting, abuse detection at the platform layer.
• Cost and performance optimization: model selection strategy, quantization/distillation where relevant, caching, and spend visibility across teams.
• Partner directly with product/ML teams to understand real usage patterns and evolve the platform based on actual production pain, not theoretical needs.

Company Description

The Ksquare Group is a consulting company focused on delivering cutting‑edge technological solutions through customized projects that enhance decision‑making with speed and quality. By partnering with leading technology platforms, the company accelerates digital transformation initiatives for its clients. Its core expertise includes Salesforce and data solutions, helping organizations modernize processes and leverage insights for better business outcomes. Professionals joining The Ksquare Group can expect to work in a dynamic environment centered on innovation, collaboration, and high‑impact technology delivery.

Role Summary

• Design and build the AI platform core: model routing/gateway layer, prompt/version management, RAG pipelines, embedding infrastructure, and vector store operations at scale.
• Own model lifecycle infrastructure: fine‑tuning pipelines, evaluation harnesses, A/B testing frameworks for model/prompt changes, and regression detection when models or prompts change.
• Build serving infrastructure: low‑latency inference paths, caching layers, batching strategies, fallback/routing logic across multiple model providers (OpenAI, Anthropic, open‑source/self‑hosted).
• Establish observability for AI systems: tracing, token/cost tracking, hallucination/quality monitoring, drift detection — the stuff that’s invisible until it breaks in prod.
• Create internal developer tooling: SDKs, APIs, and abstractions so product teams can consume AI capabilities without reinventing infra each time.
• Data and retrieval infrastructure: chunking strategies, embedding pipelines, hybrid search, freshness/index management for RAG systems.
• Guardrails and safety infra: content filtering, PII redaction, rate limiting, abuse detection at the platform layer.
• Cost and performance optimization: model selection strategy, quantization/distillation where relevant, caching, and spend visibility across teams.
• Partner directly with product/ML teams to understand real usage patterns and evolve the platform based on actual production pain, not theoretical needs.

What You Need to Bring (Non‑Negotiable)

• Strong software engineering fundamentals — this is an infrastructure/platform role first, AI second. You should be comfortable owning distributed systems, APIs, and production reliability.
• Hands‑on experience building (not just using) at least one of: RAG pipelines, model serving infra, fine‑tuning pipelines, or LLM orchestration frameworks.
• Solid understanding of how LLMs actually work under the hood — tokenization, context windows, embeddings, attention basics — enough to make real tradeoffs, not just quote them.
• Experience with at least one vector database (pgvector, Pinecone, Weaviate, Qdrant, etc.) in a real production setting.
• Production experience with observability/monitoring for ML or AI systems.
• Comfortable in Python (and ideally one of Go/Rust/TypeScript for platform‑side services).
• Experience with cloud infra (AWS/GCP/Azure) and containerized deployment (Docker/Kubernetes).