INDStaff Software Engineer - AI

IND Staff Software Engineer - GCC011
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.

Position Summary
We are seeking a highly skilled T7 AI Engineer to join our engineering team in Hyderabad, India. This role combines hands-on AI/ML engineering with deep software development expertise to build, deploy, and operate production-grade AI systems at enterprise scale. You will design and implement AI-powered solutions — from LLM integrations and agentic workflows to ML pipelines and intelligent automation — while driving AI adoption and engineering excellence across teams.

Level: T7 (Senior Engineer)
Location: Hyderabad, India
Employment Type: Full-Time
Key Responsibilities
AI/ML Engineering & Delivery

- Design, build, and deploy production AI systems including RAG pipelines, agentic workflows, multi-model orchestration, and intelligent automation

- Integrate large language model (LLM) APIs and AI/ML services into enterprise applications (GCP Vertex AI)

- Implement and optimize prompt engineering strategies, fine-tuning pipelines, embeddings, and vector search solutions

- Build and maintain AI orchestration workflows using frameworks such as LangChain, Semantic Kernel, AutoGen, CrewAI, or LlamaIndex

- Develop custom AI agents, tools, and autonomous workflows that solve real business problems

- Establish evaluation frameworks for AI systems — measuring accuracy, latency, cost, hallucination rates, and business outcomes
Full Stack Development & Integration

- Build end-to-end AI-powered applications spanning frontend, backend, APIs, and data layers

- Develop robust backend services using Python (FastAPI/Django) or Node.js to support AI workloads

- Implement and optimize RESTful APIs, GraphQL endpoints, and event-driven integrations for AI services

- Build modern frontend interfaces for AI-powered features using React, Angular, or Vue.js with TypeScript

- Write clean, well-tested, production-ready code with a focus on maintainability and operational excellence
MLOps & AI Infrastructure

- Design and implement MLOps/LLMOps pipelines for reliable model deployment, versioning, and lifecycle management

- Configure and manage cloud-native AI infrastructure (AWS, GCP) including model serving, orchestration, and auto-scaling

- Implement observability for AI systems — monitoring model drift, token costs, latency, throughput, and quality metrics

- Build and maintain CI/CD pipelines for AI model deployment, automated testing, and continuous evaluation

- Design for resilience: failover strategies, fallback models, circuit breakers, and graceful degradation
AI-Augmented Development

- Leverage AI coding assistants (GitHub Copilot, Cursor, Claude, etc.) to dramatically accelerate development workflows

- Use AI tools for code generation, refactoring, test writing, documentation, and code review

- Develop and maintain custom AI-powered developer tools, automations, and internal platforms

- Establish guardrails, security practices, and governance for responsible AI usage in engineering
Technical Documentation & Mentorship

- Influence engineering culture by evangelizing AI-first development practices across teams

- Train and upskill team members on effective use of AI tools, LLM integration patterns, and ML best practices

- Contribute to internal knowledge bases, tech talks, and communities of practice

- Partner with product, design, and data science teams to identify and deliver AI-driven opportunities

- Participate in architecture reviews and design discussions, ensuring AI solutions are production-ready from day one

Required Qualifications

- Experience: 8+ years of professional software engineering experience, with 2+ years focused on AI/ML solution development and delivery

- Education: Bachelor's degree in Computer Science, Software Engineering, AI/ML, or related field (or equivalent experience)

- AI/ML Expertise:
- Strong understanding of large language model architectures, capabilities, and limitations

- Proven track record building and deploying production AI systems (RAG, agents, fine-tuning, embeddings, vector search)

- Proficiency with AI orchestration frameworks (LangChain, Semantic Kernel, AutoGen, CrewAI, LlamaIndex)

- Hands-on experience with major LLM providers and platforms (OpenAI, Anthropic, Google Vertex AI, AWS Bedrock)

- Solid understanding of prompt engineering, evaluation methodologies, and AI safety/guardrails

- Programming: Expert-level proficiency in Python; strong skills in at least one additional language (Java, TypeScript/Node.js, C#/.NET)

- Cloud & Infrastructure: Hands-on experience deploying and operating AI workloads on cloud platforms (AWS, GCP, or Azure), containerization (Docker, Kubernetes), and CI/CD tooling (Jenkins, GitHub Actions)

- Data: Proficiency with SQL and NoSQL databases, vector databases (Pinecone, Weaviate, pgvector, ChromaDB), and data pipeline tools

- API Development: Proven track record building production APIs (REST, GraphQL, gRPC) and event-driven integrations

- AI Tools Proficiency: Advanced daily usage of AI coding assistants with demonstrated impact on productivity and code quality

- Testing: Strong testing practices for AI systems including model evaluation, integration testing, and automated quality checks

- Communication: Excellent written and verbal communication skills with ability to explain complex AI concepts to diverse audiences

Preferred Qualifications

- Experience building enterprise AI platforms serving multiple product teams

- Familiarity with custom model training, fine-tuning (LoRA, QLoRA), and RLHF techniques

- Experience with agent-based AI architectures and autonomous multi-step workflows

- Knowledge of AI security concerns (prompt injection, data leakage, model poisoning) and mitigation strategies

- Experience in regulated industries (insurance, finance, healthcare) with security and compliance requirements

- Contributions to open-source AI/ML projects or published technical content

- Cloud certifications (AWS Solutions Architect, GCP Professional Cloud Architect, or equivalent)

- Experience with real-time inference, streaming responses, and low-latency AI serving architectures

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