INDStaff Software Engineer - AI

thehartford; Hyderabad; On-site; INR 3,500,000 - 7,000,000; Full time

Job summary

The Hartford is seeking a high-caliber IND Staff Software Engineer (T7) in Hyderabad, India to design, build, and scale production AI systems. You will own AI/ML engineering and contribute to full‑stack integrations, MLOps, and governance for enterprise‑grade applications.

Ideal candidates have 8+ years in software engineering with AI/ML delivery, solid Python skills, and hands-on experience with leading AI tooling and cloud platforms.

Qualifications

• 8+ years in software engineering with AI/ML delivery experience.
• Bachelor's degree in CS or related field.
• Strong understanding of LLM architectures and safety.
• Proficient with Python and at least one other language.

Responsibilities

• Design, build, and deploy production AI systems at scale.
• Integrate LLM APIs and AI services into enterprise apps (GCP/AWS).
• Develop AI agents, tools, and autonomous workflows.
• Establish evaluation frameworks for AI systems and workflows.

Skills

AI/ML solution development

Software engineering

Python programming

LLM orchestration

Cloud deployment

CI/CD tooling

Education

Bachelor's degree in Computer Science

Tools

LangChain

Semantic Kernel

AutoGen

CrewAI

LlamaIndex

Docker

Kubernetes

Job description

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