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NowVertical

Data analytics software and services provider

Senior AI Engineer

AI Application Engineering +1Updated on Nov 13
Full-timeNavi Mumbai, Maharashtra, IndiaNo salary listedIn-person

About the Job

Requirements

  • 5–8 years of software engineering experience, including at least 3 years focused on production LLM-based or AI systems.
  • A proven track record building and shipping RAG pipelines, autonomous agents, and multi-step reasoning chains.
  • Hands-on experience with Google AI SDKs, Vertex AI, and/or Azure AI services.
  • Proficiency with orchestration frameworks such as LangGraph, CrewAI, LlamaIndex, Haystack, or comparable frameworks.
  • Expert-level Python and strong backend development skills using FastAPI, Go, or Node.js.
  • Understanding of agent design patterns, including planning, reflection, memory, and tool use.
  • Experience integrating complex enterprise APIs and event-driven systems into agentic workflows.
  • Ability to trace, debug, and improve non-deterministic, multi-step AI reasoning pipelines.
  • Ability to build resilient, observable, production-ready AI systems.
  • Familiarity with GCP and/or Azure core services, including GKE, Cloud Run, and Azure AI services.
  • Infrastructure-as-code experience with Terraform or Pulumi.
  • Experience building automated evaluation and deployment pipelines for AI models as part of CI/CD.
  • Experience with vector databases such as Vertex AI Vector Search, Azure AI Search, Pinecone, or Weaviate.
  • Experience with data pipelines and tools such as BigQuery, Pub/Sub, or Azure Synapse.
  • ETL/ELT experience preparing unstructured data for RAG and fine-tuning.

Responsibilities

  • Lead implementation of multi-agent systems using Google AI SDKs, including Vertex AI Agent Builder, LangGraph, CrewAI, and other orchestration frameworks.
  • Design and build stateful, tool-augmented agents for advanced reasoning, long-term planning, and autonomous execution.
  • Develop and document agent orchestration patterns, including planner-executor, supervisor-worker, and hierarchical agent structures.
  • Implement short-term, long-term, and cross-session contextual memory systems.
  • Enable cross-agent communication and multimodal coordination.
  • Lead delivery of production-grade LLM applications, including RAG pipelines, specialised agents, and developer copilots.
  • Integrate diverse tools, enterprise APIs, and legacy systems into agentic workflows.
  • Design system prompts, dynamic routing logic, and AI guardrails using Vertex AI Model Garden or Azure AI Studio.
  • Optimize AI workflows for latency, token cost, and output quality.
  • Develop and own reusable AI microservices, agent frameworks, and standardized APIs.
  • Contribute to AI platform capabilities, including model routing, centralized observability, and safety filters.
  • Define and enforce AI engineering standards and best practices across the team.
  • Deploy and manage agent-based systems on GCP, Azure, and/or AWS using Docker, Kubernetes (GKE/AKS/EKS), and Cloud Run.
  • Implement monitoring and observability using Vertex AI Inspector, LangSmith, or Azure Monitor.
  • Lead incident response and post-mortems for production AI system failures.
  • Act as technical lead on key AI engineering workstreams, shaping architecture and approach.
  • Mentor junior AI engineers through code review, design discussions, and pair programming.
  • Collaborate with the Principal AI Engineer and data, product, and delivery teams to align AI engineering with business outcomes.
  • Keep current with agentic AI developments and bring new approaches into the team.

About the Company

NowVertical

Data analytics software and services provider

NowVertical provides data analytics software services to help clients make data-informed decisions. Its ecosystem includes tools like NOW Analytics Toolkit, NowFusion, NowPrivacy, and NowDataBench for data management, privacy, and analytics, supporting diagnostic, predictive, and prescriptive models with consulting and support. The company stands out by acquiring leading data and analytics firms and keeping them autonomous, blending proprietary software with expert services. Its goal is to help private industry and government maximize data value and succeed in the digital economy using scalable, cost-efficient analytics.

Company size
201-500
Company stage
IPO
Headquarters
Toronto, Canada
Founded
2017

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