INDStaff Software Engineer - AI

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.

About Thehartford

• 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.
• Us | Our Culture | What It’s Like to Work Here

Responsibilities

• 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
• 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
• 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

Requirements

• 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
• 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
• 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

Testing

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