Job Description

We are looking for an AI Platform Senior Lead to join our engineering team and take ownership of the infrastructure, pipelines, and platform capabilities that power our AI and LLM-based solutions. This is a hands-on engineering role for someone who thrives at the intersection of cloud infrastructure, data engineering, and AI systems — not someone who builds models, but someone who builds the platforms that make AI systems reliable, observable, and scalable in production.

You will work closely with application teams, data scientists, and product stakeholders to ensure AI workloads run efficiently, cost-effectively, and at scale.

What You Will Do:

AI Platform & Infrastructure

Design, build, and maintain scalable platforms that support LLM inference, embedding pipelines, RAG systems, guardrails, and multi-tenant AI services
Architect and manage APIs and microservices that abstract AI capabilities for consumption by application teams
Own platform reliability — uptime, latency SLAs, cost per request, and performance benchmarks
Build and maintain multi-tenant infrastructure with strong isolation, quota management, and usage tracking across teams and clients

Data Engineering & Processing

Design and implement data ingestion, transformation, and processing pipelines that feed AI systems
Build and manage vector databases, document stores, and retrieval infrastructure for RAG and semantic search use cases
Ensure data quality, lineage, and governance across AI data pipelines
Optimize data pipelines for throughput, latency, and cost at scale

Cloud & DevOps

Own cloud infrastructure on AWS — including ECS/EKS, Lambda, API Gateway, S3, RDS, SQS, and AI/ML services like Bedrock and SageMaker
Implement Infrastructure as Code using Terraform or CDK
Build CI/CD pipelines for AI workloads including model serving, evaluation, and deployment automation
Manage Kubernetes clusters and containerised workloads for AI services

Observability & Operations

Implement comprehensive observability for AI systems — logging, tracing, metrics, cost dashboards, and alerting
Build evaluation pipelines to monitor LLM output quality, hallucination rates, latency, and token consumption in production
Proactively identify and resolve performance bottlenecks, reliability gaps, and cost inefficiencies

Collaboration & Technical Leadership

Partner with product, architecture, and application teams to translate requirements into scalable platform capabilities
Define and enforce platform engineering standards, patterns, and best practices
Mentor junior engineers and contribute to technical design reviews

Must Have

8–12 years of hands-on software and platform engineering experience
Strong proficiency in Python — async patterns, API development, microservices
Deep experience with cloud platforms — AWS preferred (ECS/EKS, Lambda, API Gateway, S3, SQS, RDS)
Solid data engineering background — pipeline design, ETL/ELT, vector databases (Pinecone, Weaviate, pgvector), document processing
Experience building and operating production AI or LLM-based systems — API gateways, embedding pipelines, RAG architectures, inference serving
Strong understanding of multi-tenancy, rate limiting, quota management, and tenant isolation patterns
Experience with containerisation and orchestration — Docker, Kubernetes, Helm
Hands-on with observability tooling — Prometheus, Grafana, OpenTelemetry, Datadog or similar
Infrastructure as Code experience — Terraform or AWS CDK
Hands-on experience with LLM orchestration frameworks — LangChain, LangGraph, or similar — to understand agentic patterns, tool use, and multi-agent workflows at a platform level
Knowledge of AI guardrails, content filtering, and responsible AI patterns in production — including toxicity filtering, PII detection, and output validation

Good To Have

Experience with LLM evaluation frameworks — RAGAS, LangSmith, or custom eval pipelines
Experience with voice AI pipelines or multimodal systems
AWS certifications — Solutions Architect, ML Engineer, or equivalent

Mandatory Skills: Python , Prometheus , Docker

Years Of Experience: 8 to 12 Years

Senior Technical Lead