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Salary
$37k – $92k per year (Estimated)
Location
In office (Bengaluru)
Seniority
Senior
Employment
Full-Time
Overview
Company
Impact
Profile match
Weekday is an Indian recruitment company that sources software engineers through referrals from other engineers rather than through job advertisements or agency databases. Its model pays working engineers to vouch for former colleagues they rate, turning informal knowledge about who is genuinely good into a searchable candidate pool that companies can hire from. Based in Bengaluru and backed by Y Combinator, the platform has layered AI screening and outbound sourcing on top of that referral network, and sells to startups and technology companies hiring in the Indian market.

This role is for one of Weekday’s clients

Salary range: Rs 5000000 - Rs 15000000 (ie INR 50 - 150 LPA)

Min Experience: 2+ years

Location: Bengaluru, Karnataka, India

JobType: full-time

We are looking for a highly skilled and innovative Senior AI Engineer to design, build, and deploy intelligent AI systems with a strong focus on agent orchestration. The ideal candidate will have hands-on experience developing AI-powered applications, autonomous agents, and multi-agent workflows that can reason, plan, use tools, and execute complex tasks reliably.

You will work closely with engineering, product, and data teams to transform business requirements into scalable AI solutions. This role requires strong software engineering fundamentals, practical knowledge of modern AI technologies, and the ability to build production-ready agentic systems.

Requirements

Key Responsibilities

  • Design, develop, and deploy intelligent AI agents capable of reasoning, planning, decision-making, and autonomous task execution.
  • Build robust agent orchestration frameworks for coordinating multiple agents, tools, APIs, workflows, and external systems.
  • Develop multi-agent architectures where specialized agents collaborate to solve complex business problems.
  • Design agent workflows involving task decomposition, tool calling, memory, context management, routing, state management, and human-in-the-loop interactions.
  • Integrate Large Language Models (LLMs) with enterprise applications, APIs, databases, and third-party tools.
  • Develop reliable mechanisms for agent evaluation, monitoring, observability, error handling, retries, and fallback strategies.
  • Optimize AI systems for accuracy, latency, scalability, reliability, and cost efficiency.
  • Build APIs and backend services that support AI agents and intelligent automation workflows.
  • Experiment with emerging AI models, agent frameworks, retrieval techniques, and orchestration patterns.
  • Collaborate with product managers, software engineers, and data teams to identify opportunities for AI-driven automation.
  • Establish engineering best practices for testing, deployment, versioning, security, and responsible AI development.

Must-Have Skills

  • Strong hands-on expertise in AI agent orchestration and agentic AI systems.
  • Experience designing multi-step and multi-agent workflows.
  • Strong understanding of LLMs, prompt engineering, tool/function calling, context management, and AI workflows.
  • Proficiency in at least one programming language such as Python, Java, JavaScript, or TypeScript.
  • Experience integrating LLMs with APIs, databases, enterprise systems, and external tools.
  • Strong understanding of software architecture, APIs, microservices, and distributed systems.
  • Experience building and deploying production-grade AI applications.
  • Strong problem-solving, debugging, and analytical skills.

Good-to-Have Skills

  • Experience with frameworks such as LangChain, LangGraph, AutoGen, CrewAI, Semantic Kernel, or similar agent orchestration technologies.
  • Knowledge of RAG, vector databases, embeddings, semantic search, and knowledge systems.
  • Experience with cloud platforms such as AWS, Azure, or GCP.
  • Familiarity with Docker, Kubernetes, CI/CD, and MLOps practices.
  • Experience with AI evaluation frameworks, observability, and agent performance monitoring.
  • Knowledge of fine-tuning, model optimization, and inference infrastructure.
  • Exposure to multimodal AI, voice agents, or AI automation platforms.
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