Lead Software Engineer Agentic AI Platform & AI Research

WNS Holdings; Bengaluru; On-site; INR 2,600,000 - 3,800,000; Full time

Job summary

WNS Holdings in Bengaluru is seeking a Senior Software Engineer / Lead Engineer to design, deploy, and operate production AI systems, focusing on Agentic AI platforms and GenAI solutions using Python, FastAPI, and microservices.

You will design agent runtimes, memory and context management, RAG workflows, AI governance, and cloud-native engineering with Docker, Kubernetes, and CI/CD to deliver scalable, reliable enterprise-grade AI services.

Qualifications

• 6-8 years of software engineering experience with strong backend development.
• 2+ years building and operating GenAI/Agentic AI applications in production.
• Strong Python development with FastAPI, Pydantic, AsyncIO, APIs, and microservices.
• Experience with OpenAI, Azure OpenAI, Anthropic Claude, or Google Gemini.
• Agentic AI architectures, multi-agent systems, tool calling, workflow orchestration, MCP, and RAG frameworks.
• Hands-on experience with embeddings, vector databases, hybrid retrieval, reranking, memory and context management.
• Strong knowledge of distributed systems, concurrency, idempotency, fault tolerance, failure recovery.
• Experience with PostgreSQL, Redis and/or MongoDB, Docker & Kubernetes, and CI/CD (GitHub Actions, GitLab CI).
• Strong knowledge of API design, testing automation, code quality, observability, and production operations.

Responsibilities

• Design and develop production-grade Agentic AI workflows and multi-agent systems.
• Build reliable agent runtimes with planning, orchestration, tool execution, memory, approvals, and recovery mechanisms.
• Implement structured outputs, tool/function calling, workflow orchestration, and deterministic execution patterns.
• Define engineering best practices for scalable and governed AI systems.
• Design and implement enterprise-scale RAG solutions, memory and context-management across AI workflows.
• Optimize retrieval quality using embeddings, hybrid search, reranking, and citation grounding.
• Establish monitoring, evaluation, observability, and auditability for production AI systems.
• Drive performance tuning, reliability, testing, incident management, and cost optimization.
• Develop enterprise integrations and MCP-based tool interfaces; observability with logs, metrics, monitoring and tracing.

Skills

Backend development

GenAI/Agentic AI

Python

FastAPI

Pydantic

AsyncIO

APIs & microservices

OpenAI/Azure OpenAI/Anthropic/Google

Agentic AI architectures

Multi-Agent Systems

Tool/Function Calling

Workflow Orchestration

MCP (Model Context Protocol)

RAG Frameworks

Embeddings

Vector Databases

Hybrid Retrieval/Reranking

Memory/Context Management

Distributed Systems

Concurrency

Idempotency

Fault Tolerance

PostgreSQL

Redis

MongoDB

Docker

Kubernetes

CI/CD (GitHub Actions, GitLab)

API Design

Testing Automation

Observability/Production Ops

Tools

PostgreSQL

Redis

MongoDB

Docker

Kubernetes

CI/CD

GitHub Actions

GitLab CI

Job description

Role Overview

We are looking for a highly skilled Senior Software Engineer / Lead Engineer to build and scale enterprise-grade Agentic AI platforms and GenAI solutions. The ideal candidate should have hands‑on experience designing, deploying, and operating production AI systems, with strong expertise in Python, Agentic AI, RAG, Distributed Systems, Cloud‑Native Engineering, and AI Governance.

Key Responsibilities

Agentic AI & Platform Engineering

• Design and develop production-grade Agentic AI workflows and multi-agent systems.
• Build reliable agent runtimes with planning, orchestration, tool execution, memory, approvals, and recovery mechanisms.
• Implement structured outputs, tool/function calling, workflow orchestration, and deterministic execution patterns.
• Define engineering best practices for scalable and governed AI systems.

RAG, Memory & Context Management

• Design and implement enterprise-scale RAG solutions.
• Build memory and context-management capabilities across AI workflows.
• Optimize retrieval quality using embeddings, hybrid search, reranking, and citation grounding.

AI Governance & Reliability

• Implement controls for prompt injection, hallucinations, PII protection, RBAC, tenant isolation, and policy enforcement.
• Establish monitoring, evaluation, observability, and auditability for production AI systems.
• Drive performance tuning, reliability, testing, incident management, and cost optimization.

Cloud & Platform Engineering

• Build and operate scalable AI services using Python, Docker, Kubernetes, and CI/CD.
• Develop enterprise integrations and MCP-based tool interfaces.
• Implement observability using logs, metrics, monitoring, and distributed tracing.

Mandatory Skills

• 6-8 years of software engineering experience with strong backend development expertise.
• 2+ years of hands‑on experience building and operating GenAI / Agentic AI applications in production.
• Strong Python development experience using FastAPI, Pydantic, AsyncIO, APIs, and microservices architecture.
• Experience with OpenAI, Azure OpenAI, Anthropic Claude, or Google Gemini.
• Strong experience in:
  • Agentic AI Architectures
  • Multi-Agent Systems
  • Tool/Function Calling
  • Workflow Orchestration
  • MCP (Model Context Protocol)
  • RAG Frameworks
• Hands‑on experience with:
  • Embeddings
  • Vector Databases
  • Hybrid Retrieval & Reranking
  • Memory & Context Management
• Strong understanding of:
  • Distributed Systems
  • Concurrency
  • Idempotency
  • Fault Tolerance
  • Failure Recovery
• Experience with:
  • PostgreSQL
  • Redis and/or MongoDB
  • Docker & Kubernetes
  • CI/CD Pipelines (GitHub Actions, GitLab CI, etc.)
• Strong knowledge of:
  • API Design
  • Testing Automation
  • Code Quality
  • Observability
  • Production Operations

Preferred Skills

• Experience with LangGraph, CrewAI, AutoGen, Semantic Kernel, OpenAI Agents SDK, DSPy, or Google ADK.
• Exposure to LLMOps and AI Observability tools such as Langfuse, TrueFoundry, Arize Phoenix, Ragas, DeepEval, or Promptfoo.
• Experience with AI Governance and Security frameworks such as Presidio, Guardrails AI, NeMo Guardrails, OPA, or Cedar.
• Experience with AWS, Azure, or GCP.
• Exposure to BFSI, Insurance, Healthcare, or other regulated industries.