Equifax is hiring for Software Engineer

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Job Details

  1. Company: Equifax
  2. Job Position: Lead Software Engineer
  3. Job Location: Trivandrum, Kerala / Pune, Maharashtra, India
  4. Job Type: Full-time
  5. Work Mode: Hybrid
  6. Experience: 8+ years in software engineering, including at least 2 years building production-grade LLM or Generative AI applications
  7. Qualification: A relevant technical degree is beneficial; a specific mandatory degree is not mentioned in the provided description.
  8. Salary: Estimated ₹30 LPA–₹55 LPA, depending on experience, AI engineering expertise, and location. This is an indicative estimate, not an officially disclosed salary range.
  9. Job Category: Technology, Software Engineering, and Agentic AI
  10. Job ID: J00179327
  11. Posting Date: October 13, 2026

Job Overview

Equifax is hiring a Lead Software Engineer to design and develop advanced Agentic AI platforms for enterprise applications. The role focuses on building autonomous AI agents that can reason, plan tasks, select tools, and execute complex workflows. The selected candidate will use Python, LangGraph, and other agent orchestration frameworks to develop secure, scalable, and reliable AI systems. Responsibilities also include designing system architecture, improving performance, implementing security controls, establishing automated evaluation processes, and guiding an engineering team. This opportunity is suitable for experienced software engineers with strong Generative AI and production deployment expertise.

Key Responsibilities

  1. Design and build stateful multi-agent workflows using frameworks such as LangGraph, AutoGen, and CrewAI.
  2. Develop AI agents that support reasoning, planning, task delegation, and execution.
  3. Build secure integrations between LLMs and enterprise databases, ERP systems, and CRM platforms.
  4. Develop production-ready orchestration services using Python, FastAPI, and Pydantic.
  5. Optimize context windows through routing, chunking, and compression techniques.
  6. Improve application performance by managing latency, computing costs, and resource utilization.
  7. Implement security controls to prevent hallucinations, prompt injection, and unauthorized tool usage.
  8. Develop automated AI evaluation pipelines using LLM-as-a-judge and deterministic testing approaches.
  9. Implement tracing and observability to monitor agent workflows and identify execution failures.
  10. Prevent infinite loops and improve the reliability of multi-step AI processes.
  11. Establish engineering standards and document architectural decisions.
  12. Mentor engineers through pair programming, code reviews, and technical guidance.
  13. Collaborate with delivery managers on sprint planning and project execution.
  14. Deploy and maintain cloud-native AI applications using Docker and Kubernetes.
  15. Ensure AI applications meet enterprise requirements for security, scalability, and reliability.
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Eligibility Criteria

  1. Candidates should have at least 8 years of core software engineering experience.
  2. At least 2 years of experience building and deploying production-grade LLM applications or Generative AI systems is required.
  3. Strong Python programming skills are essential.
  4. Hands-on experience with multi-agent orchestration frameworks is required.
  5. Experience with FastAPI, Pydantic, and vector databases is expected.
  6. Knowledge of state management, AI evaluation, and semantic guardrails is required.
  7. Understanding of prompt security and non-deterministic system testing is essential.
  8. Experience with Docker and Kubernetes is required.
  9. Candidates should have experience deploying AI solutions on AWS, Azure, or Google Cloud.
  10. Prior experience with multi-tenant AI platforms or regulated industries is an advantage.

Required Skills

  1. Python programming and backend development.
  2. Generative AI and large language models (LLMs).
  3. Agentic AI architecture and multi-agent systems.
  4. LangGraph, AutoGen, CrewAI, or custom agent state machines.
  5. FastAPI and Pydantic.
  6. Vector databases and embedding technologies.
  7. Prompt engineering and context-window optimization.
  8. Semantic guardrails and prompt injection prevention.
  9. AI model evaluation and automated testing.
  10. Docker and Kubernetes.
  11. AWS, Azure, or Google Cloud.
  12. AI observability, tracing, and performance optimization.
  13. Enterprise API and database integrations.
  14. Software architecture and technical leadership.
  15. Mentoring, code reviews, and stakeholder communication.

Why Join Equifax?

  1. Work with advanced Agentic AI and enterprise Generative AI technologies.
  2. Lead the architecture and development of production-grade AI platforms.
  3. Collaborate with experienced engineers and technical teams.
  4. Develop expertise in autonomous AI agents and cloud-native systems.
  5. Help establish engineering standards and improve software quality.
  6. Work on enterprise solutions where security, reliability, and scalability matter.
  7. Access learning opportunities and guided career development tracks.
  8. Benefit from a hybrid work environment, healthcare packages, and paid time off.
  9. Contribute to a global data, analytics, and technology company.
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