Senior AI Engineer - India

NowVertical NA & EMEA; Navi Mumbai; On-site; INR 3,000,000 - 5,400,000; Full time

Benefits offered by this job

Competitive salary

Global client exposure

Job summary

Acrotrend - A NowVertical Company in Navi Mumbai, India, is seeking a Senior AI Engineer to architect and deliver production‑grade AI systems. You will lead multi‑agent platforms, LLM orchestration, and platform APIs across cloud environments.

The role emphasises hands‑on development with Google Vertex AI, Azure AI, and GKE, plus mentoring junior engineers and shaping scalable, observable AI infrastructure for global clients.

Qualifications

• 5–8 years of software engineering experience with at least 3 years focused on AI/LLM in production.
• Proven track record shipping RAG pipelines, autonomous agents, and multi-step reasoning.
• Strong hands-on with Google AI SDKs, Vertex AI and/or Azure AI services.

Responsibilities

• Lead the implementation of multi-agent systems using Google AI SDKs and Vertex AI.
• Design and build memory systems (short-, long-, cross-session) for agentic apps.
• Develop and document agent orchestration patterns (planner-executor, supervisor-worker).
• Build and maintain reusable AI microservices and standardized APIs.
• Deploy agents on GCP/Azure/AWS with Docker, Kubernetes, and Cloud Run.
• Mentor junior engineers and collaborate with cross-functional teams.

Skills

Python

LLM systems

RAG pipelines

Multi-agent systems

Vertex AI

Google AI SDKs

Azure AI

Orchestration frameworks

Backend development

Tools

GKE

Cloud Run

Terraform

Kubernetes

Pulumi

LangGraph

CrewAI

Job description

Role: Senior AI Engineer

Location: Navi Mumbai, India

Experience: 5-8 Years

Responsibilities

• Agentic AI & Multi‑Agent Systems
  • Lead the implementation of multi‑agent systems using Google AI SDKs (Vertex AI Agent Builder), LangGraph, CrewAI, and other emerging orchestration frameworks
  • Design and build stateful, tool‑augmented agents capable of advanced reasoning, long‑term planning, and autonomous execution
  • Develop and document agent orchestration patterns including planner‑executor, supervisor‑worker, and hierarchical agent structures
  • Implement sophisticated memory systems (short‑term, long‑term, and cross‑session contextual memory)
  • Enable seamless cross‑agent communication and multi‑modal coordination
• LLM Applications & Orchestration
  • Lead the delivery of production‑grade LLM applications: RAG pipelines, specialised agents, and developer copilots
  • Integrate diverse tools, enterprise APIs, and legacy systems into agentic workflows
  • Design robust system prompts, dynamic routing logic, and AI guardrails using Vertex AI Model Garden or Azure AI Studio
  • Drive optimisation of AI workflows for latency, token cost, and output quality
• Platform & API Development
  • Develop and own reusable AI microservices, agent frameworks, and standardised APIs
  • Contribute to core AI platform capabilities including model routing, centralised observability, and safety filters
  • Define and enforce engineering standards and best practices for AI development across the team
• Cloud Deployment & Production Systems
  • Deploy and manage agent‑based systems on GCP, Azure, and/or AWS using Docker, Kubernetes (GKE/AKS/EKS), and Cloud Run
  • Implement comprehensive monitoring and observability using Vertex AI Inspector, LangSmith, or Azure Monitor
  • Drive incident response and post‑mortems for production AI system failures
• Technical Leadership & Mentoring
  • Act as a technical lead on key AI engineering workstreams, shaping architecture and approach
  • Mentor and support more junior AI engineers through code review, design discussions, and pair programming
  • Collaborate with Principal AI Engineer and cross‑functional teams (data, product, delivery) to align AI engineering with business outcomes
  • Stay at the forefront of the rapidly evolving agentic AI landscape and bring new approaches into the team

Qualifications & Skills

• Core AI Expertise (Required)
  • 5–8 years of software engineering experience with at least 3 years focused on LLM‑based or AI systems in production
  • Proven track record building and shipping RAG pipelines, autonomous agents, and multi‑step reasoning chains
  • Strong hands‑on experience with Google AI SDKs, Vertex AI, and/or Azure AI services
  • Deep proficiency in orchestration stacks: LangGraph, CrewAI, LlamaIndex, Haystack, or comparable frameworks
  • Expert‑level Python; strong backend development skills (FastAPI, Go, or Node.js)
• Agentic & Systems Thinking (Required)
  • Deep understanding of agent design patterns: planning, reflection, memory, and tool‑use
  • Experience integrating complex enterprise APIs and event‑driven systems into agentic workflows
  • Proven ability to trace, debug, and improve non‑deterministic, multi‑step AI reasoning pipelines
  • Strong instinct for building resilient, observable, and production‑ready AI systems
• Cloud & DevOps (Required)
  • Strong familiarity with GCP and/or Azure core services: GKE, Cloud Run, Azure AI services
  • Infrastructure as Code: Terraform or Pulumi
  • CI/CD: experience building automated evaluation and deployment pipelines for AI models
• Data Engineering (Nice to Have)
  • Vector databases: Vertex AI Vector Search, Azure AI Search, Pinecone, or Weaviate
  • Data pipelines: BigQuery, Pub/Sub, Azure Synapse
  • ETL/ELT experience preparing unstructured data for RAG and fine‑tuning

Ideal Candidate Profile

• Engineers who view LLMs as components within a larger system—not just standalone models—and who think carefully about architecture, reliability, and cost
• A senior mindset: someone who takes ownership, drives outcomes, and elevates the engineers around them
• A strong bias toward production‑ready, resilient, and observable AI applications
• Genuine passion for the rapidly evolving landscape of agentic AI and next‑generation software architectures
• Comfortable working across cloud platforms and navigating ambiguity in a fast‑moving consultancy environment

Benefits

• Competitive base salary + performance incentives
• Comprehensive benefits structure
• Exposure to global clients, cutting‑edge AI projects, and a fast‑growing AI practice
• Ongoing learning and development support