IND Senior Associate/ Engineer - AI Engineering

IND Senior Software Engineer - GCC024

We’re determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals – and to help others accomplish theirs, too. Join our team as we help shape the future.

Purpose: Recruit and assess a hands-on Java/AI engineer for the HIG India POD model, with emphasis on Java design patterns, secure API/backend delivery, Python as an adjacent technical skill, cloud readiness, AI developer tools, ownership, and business-aligned execution.

Design principle: This is one Java JD. Python is included as a supporting technical skill, not as a separate role. The expectation is strong Java engineering fundamentals, AI-assisted delivery habits, mature technical judgment, and ability to operate in an accountable POD with US + India collaboration.

Location

Hyderabad, India

Employment type

Full-time

Experience Level

4-6 years preferred; strong candidates with lower tenure may be considered if they demonstrate excellent fundamentals, learnability, and analytical problem solving.

Role family

Software Engineering - Application Development

Operating model

HIG India POD / Agile delivery team with strong collaboration across US and India

Core expectation

Strong Java engineering coupled with Python-based AI development, GenAI solution delivery, API integration, cloud-native architecture, AI developer tools, and accountable POD ownership. Candidate should be capable of designing, developing, deploying, and operating enterprise AI-enabled applications and intelligent workflows while maintaining secure, scalable, and compliant software solutions.

Position Overview

The Employee Benefits IT team is seeking a Software Engineer with strong Java, Python, API engineering, and AI development capabilities to support HIG India POD initiatives across application modernization, intelligent automation, GenAI solutions, cloud-native architectures, and enterprise platform delivery.

The ideal candidate combines strong software engineering fundamentals with practical AI implementation experience, including large language models (LLMs), AI orchestration frameworks, RAG-based solutions, prompt engineering, and production AI governance. The engineer should be comfortable building secure APIs, integrating enterprise systems, operationalizing AI solutions, and collaborating across Product, Architecture, QE, DevOps, Data, and Business teams.

Key Responsibilities

• Design, develop, test, deploy, and support enterprise applications using Java, Spring Boot, Python, and cloud-native technologies.
• Build and maintain REST APIs, event-driven integrations, and scalable backend services.
• Design and implement end-to-end AI solutions including:
• Data ingestion pipelines
• Retrieval-Augmented Generation (RAG)
• Agentic workflows
• Model integrations
• Enterprise AI service orchestration
• Develop AI-powered applications using LLMs, prompt engineering techniques, and semantic search capabilities.
• Implement AI governance controls, guardrails, monitoring, evaluation frameworks, and responsible AI practices.
• Integrate AI services with downstream applications, APIs, databases, and enterprise platforms.
• Use AI developer tools responsibly to accelerate coding, testing, code analysis, documentation, and troubleshooting.
• Participate in Agile ceremonies and contribute to architecture discussions, technical design reviews, and production readiness assessments.
• Collaborate with business stakeholders to understand customer journeys, operational workflows, and automation opportunities.
• Troubleshoot production issues using logs, monitoring tools, telemetry, AI observability platforms, and structured root-cause analysis.
• Contribute to engineering standards related to software quality, AI governance, security, performance, and reliability

Technical Knowledge Requirements

Area

Required Capability

What Good Looks Like

Java Engineering

Java 17+, Spring Boot, APIs, SOLID, Design Patterns

Builds secure, scalable enterprise services

Python Engineering

Python development, automation frameworks, data processing

Develops AI workflows, analytics, and automation solutions

AI & GenAI

LLMs, Prompt Engineering, AI Evaluation

Delivers AI solutions aligned to business outcomes

Agentic AI

Multi-agent orchestration and workflow design

Designs autonomous AI workflows and process automation

RAG Architecture

Semantic retrieval, vector databases, grounding

Reduces hallucinations and improves response quality

API Integration

REST, event-driven services, integrations

Connects AI services with enterprise systems

Data Platforms

SQL, data modeling, analytics pipelines

Supports AI and operational workloads

Cloud & AI Platforms

AWS, GCP, Vertex AI, containers

Deploys production-grade AI solutions

AI Governance

AI monitoring, guardrails, model evaluation

Ensures responsible and compliant AI usage

DevOps & MLOps

CI/CD, GitHub, observability, model deployment

Operates AI and application workloads reliably

AI Developer Tools

GitHub Copilot, Copilot Chat, AI assistants

Uses AI responsibly with human oversight

QE & Automation

JUnit, Playwright, API automation, pytest

Quality-first engineering mindset

Leadership Expectations

Area

Required Capability

What good looks like in the HIG India POD

Ownership

Takes accountability for outcomes, not only assigned tasks.

Raises risks early, follows through on commitments, and drives blockers to closure.

Technical judgment

Uses design thinking and tradeoff analysis to choose maintainable solutions.

Can explain options, risks, and recommended approach to tech leads and stakeholders.

Collaboration

Works across US/India time zones and functions without waiting for perfect instructions; learns adjacent QE practices such as Playwright and contributes where needed.

Brings questions, proposals, and artifacts that make decisions easier.

Mentoring

Helps peers with code reviews, debugging, standards, and onboarding.

Improves POD capability through examples, documentation, and pairing.

Bias for action

Moves work forward with appropriate urgency while respecting enterprise controls.

Breaks ambiguous problems into next steps and demonstrates follow-through.

Business Acumen Expectations

Area

What Good Looks Like In The HIG India POD

Outcome orientation

Understands how technical work supports claimant, employer, operations, and product outcomes.

Frames decisions around stability, speed, user experience, and business risk.

Domain awareness

Develops working knowledge of Employee Benefits, claims, intake, leave, payments, profile, and service workflows relevant to the POD.

Can connect defects/features to user journeys and operational impact.

Risk and controls

Understands compliance, data protection, security, accessibility, and production-readiness expectations.

Plans for controls during development rather than treating them as afterthoughts.

Financial awareness

Understands HIG India is a strategic capacity and vendor-reduction lever.

Demonstrates productivity, knowledge transfer, and reduced dependency on external SMEs.

Communication

Communicates clearly with business/technology partners using concise status, risks, decisions, and asks.

Can explain technical topics in business-friendly language.

Required Skills & Experience

Software Engineering

• 5+ years of software engineering experience.
• Strong Java development experience with Spring Boot and enterprise API development.
• Strong understanding of SOLID principles, design patterns, and microservices architecture.
• Hands-on experience developing RESTful APIs and service integrations.
• Experience with SQL, database troubleshooting, and enterprise application support.

Python Development

• Strong Python development experience for automation, analytics, AI workflows, and backend services.
• Experience using Python libraries such as:
• Pandas
• NumPy
• FastAPI
• PyTest
• LangChain/LangGraph (preferred)

AI & GenAI Experience

• Demonstrated experience designing, developing, and deploying AI solutions in production environments.
• Experience building:
• AI pipelines
• AI agents
• Enterprise AI integrations
• Event-driven AI workflows
• Experience tuning, testing, evaluating, and optimizing AI solutions.
• Experience implementing AI governance controls and responsible AI practices.
• Experience working with enterprise LLM platforms.

AI Modeling Skills

• Natural Language Processing (NLP)
• Intent Classification
• Prompt Engineering
• LLM Evaluation Frameworks
• Retrieval-Augmented Generation (RAG)
• Agentic AI Architecture
• Risk Assessment and Confidence Evaluation
• AI Monitoring and Operational Excellence

Cloud & Platform Skills

• AWS (preferred) or GCP experience.
• Containerization technologies (Docker/Kubernetes).
• CI/CD pipelines.
• GitHub and GitHub Actions.
• Cloud observability and monitoring tools.

Preferred / Nice-to-Have Skills

• Java 17/21, Spring Boot 3, Spring 6, Tomcat, containerization or cloud migration experience.
• Vertex AI, Gemini, OpenAI, Azure OpenAI, Anthropic, or similar AI platforms.
• Vector databases such as Pinecone, Weaviate, Chroma, or Vertex AI Search.
• LangChain, LangGraph, Semantic Kernel, CrewAI, or equivalent orchestration frameworks.
• MLOps and AI Operations experience.
• Insurance, Benefits, Claims, Leave Management, or Financial Services domain experience.
• Experience building enterprise copilots and intelligent workflow solutions.