Software Engineer - AI Platform Services

Tekskills Inc.; Mumbai; On-site; INR 700,000 - 1,100,000; Full time

Job summary

Tekskills Inc. in Mumbai seeks an experienced software engineer to design, build, and operate core platform services powering internal AI capabilities.

You will own gateway concerns end-to-end, including routing, timeouts, streaming, rate limits, and multi-tenancy, while maintaining safety and high reliability on Kubernetes. The role requires strong backend skills with Python/Go/Java, knowledge of async patterns, and experience with distributed systems.

Qualifications

• 7+ years of professional software engineering experience.
• Strong expertise in Python, Java, or Go with async patterns and building high-throughput services.
• Solid distributed systems fundamentals: idempotency, backpressure, retries/timeouts.
• Production experience operating services on Kubernetes with observability.
• Familiarity with LLM integration patterns (streaming responses).
• Demonstrated design leadership (RFCs, architecture reviews).
• Excellent communication to translate tradeoffs to stakeholders.

Responsibilities

• Design, build, and operate core platform services powering internal AI capabilities.
• Own gateway/platform concerns end-to-end: routing, timeouts, streaming, rate limits, multi-tenancy, safety.
• Build and operate MCP capabilities as part of the platform.
• Scale and ensure availability on Kubernetes: autoscaling, rollout strategies, performance tuning.
• Raise reliability practices: SLOs/SLIs, incident response, post-incident follow-ups.
• Improve delivery safety: CI/CD, IaC-driven repeatability, secure SDLC.

Skills

Python

Go

Java

Async patterns

Distributed systems

Kubernetes

Communication

Tools

Kubernetes

Datadog/OpenTelemetry

Terraform

AWS Cloud

Job description

Key Responsibilities

• Design, build, and operate core platform services (Python; REST + async; streaming where appropriate) powering firm-wide internal AI/agentic capabilities.
• Own gateway/platform concerns end-to-end: routing, timeouts/retries, streaming, request shaping, rate limits/quotas, multi-tenancy, policy enforcement, provider abstraction, safe degradation, and robust client experience.
• Build and operate MCP capabilities as part of the platform.
• Build for scale and availability on Kubernetes: autoscaling, rollout strategies, capacity planning, performance tuning, and production debugging.
• Raise reliability practices: define and manage SLOs/SLIs, instrumentation standards, incident response/runbooks, post-incident follow-ups, load/resilience testing, and operational excellence.
• Improve delivery safety: CI/CD, environment promotion, IaC-driven repeatability, and secure SDLC practices.
• Influence roadmap and technical strategy: prioritize foundational investments and reduce platform risk for a business-critical internal platform.

Required Qualifications

• 7+ years of professional software engineering experience (or equivalent practical experience)
• Strong expertise in Python, Java, or Go, including async patterns, concurrency, and building high-throughput services (FastAPI or similar).
• Solid distributed systems fundamentals: idempotency, backpressure, failure isolation, consistency tradeoffs, rate limiting, retries/timeouts.
• Production experience operating services on Kubernetes (deployments, autoscaling, debugging, observability, performance).
• Basic familiarity with LLM integration patterns (streaming responses, tool/function calling)
• Demonstrated design leadership (RFCs, architecture reviews, leading cross-team initiatives).
• Excellent communication skillsable to translate technical tradeoffs to stakeholders and partner teams.

Preferred Qualifications

• Experience with service-to-service authentication patterns (API keys, OAuth/JWT, mTLS concepts).
• Familiarity with observability tooling (structured logs, metrics, tracing; Datadog or OpenTelemetry a plus).
• Strong fundamentals in AWS (or GCP/Azure) relevant to secure platforms (IAM, networking basics, compute, logging/monitoring patterns).
• Working proficiency with Terraform and automation-first operations (repeatable environments, policy checks, safe rollouts).
• Comfort using AI dev tools (Claude Code, Cursor, Gemini CLI) responsibly (tests, validation, secure coding).