Software Engineer 3 - GenAI

Accountabilities: • Design, develop, and scale production-ready multi-agent systems, including supervisor and router architectures, specialized agents, LangGraph workflows, tool calling, conversation memory, and checkpointing.

• Build robust and strongly typed tool contracts and data-access layers using internal APIs, ensuring that language models interpret and explain verified results rather than generate unsupported numerical information.

• Develop and maintain Retrieval-Augmented Generation (RAG) pipelines for utility tariff information and knowledge retrieval, implementing tenant-isolated embeddings and retrieval mechanisms.

• Optimize application performance, latency, reliability, and infrastructure costs through caching, prewarming, retries, circuit breakers, and intelligent model routing across different model tiers.

• Implement security and AI guardrails to protect applications against prompt injection, unauthorized access, inappropriate scope usage, and tenant data leakage.

• Ensure secure data management and privacy across multi-tenant GenAI applications.

• Develop observability and evaluation capabilities using tools such as LangSmith to monitor model behavior, agent performance, and application reliability.

• Collaborate with Software Development Engineers in Test (SDETs) to create evaluation datasets, regression testing suites, and quality criteria for production releases.

• Investigate and troubleshoot production issues using traces, logs, and performance metrics, identifying root causes and implementing long-term solutions.

• Support real-time streaming capabilities through WebSocket and Server-Sent Events (SSE), collaborating with frontend teams on seamless widget and application integration.

• Lead technical design discussions, architecture reviews, and code reviews while establishing engineering standards and development best practices.

• Mentor and support junior engineers, particularly SDE1 and SDE2 developers, helping them improve their technical skills and engineering capabilities.

• Communicate technical designs, architectural decisions, implementation trade-offs, and project progress effectively to engineering teams, product stakeholders, and leadership.

• Work closely with GenAI engineering, platform, QA/SDET, and product teams to deliver reliable, user-focused AI solutions.

Requirements:

• 6–10 years of professional software engineering experience, including at least 1–2 years of hands-on experience building and deploying production-grade LLM or Generative AI features.

• Strong programming proficiency in Python and good working knowledge of Java.

• Practical experience with agent orchestration frameworks such as LangChain, LangGraph, or equivalent technologies.

• Hands-on experience integrating Large Language Model APIs, including OpenAI, Anthropic, or Amazon Bedrock.

• Strong understanding of prompt engineering, tool/function calling, structured outputs, and LLM application development.

• Experience designing and implementing Retrieval-Augmented Generation (RAG) systems using embeddings and vector databases or stores, such as pgvector.

• Strong backend engineering and API design capabilities, with experience building scalable, high-performance, and low-latency services.

• Practical experience with AWS cloud infrastructure, containers, Kubernetes/EKS, PostgreSQL, Redis, and CI/CD pipelines.

• Familiarity with LLM evaluation techniques, LLM-as-a-judge approaches, and AI observability platforms such as LangSmith.

• Strong knowledge of system architecture, distributed systems, scalability, and performance optimization.

• Understanding of production monitoring, debugging, reliability engineering, and automated testing practices.

• Excellent analytical and problem-solving abilities, with the capacity to address complex technical challenges.

• Strong communication and collaboration skills, with experience participating in technical discussions and explaining architectural decisions to diverse stakeholders.

• Previous experience leading code reviews, contributing to engineering standards, and mentoring junior developers.

Nice to Have:

• Experience working with time-series data, Internet of Things (IoT), smart meter data, or the energy and utilities industry.

• Exposure to voice-based GenAI applications, conversational AI platforms, or Interactive Voice Response (IVR) solutions.

• Experience designing and developing multi-tenant Software-as-a-Service (SaaS) applications.

• Knowledge of application security, data privacy, tenant isolation, and security enhancements for LLM-powered systems.

• Familiarity with ELK Stack and enterprise-level monitoring and logging solutions.

Technology Stack:

• Programming languages: Python, Java.

• GenAI and orchestration: LangChain, LangGraph, OpenAI, Anthropic, Amazon Bedrock.

• AI monitoring and evaluation: LangSmith, LLM evaluation frameworks, agent guardrails.

• AI architecture: RAG, embeddings, vector databases, tool/function calling.

• Backend and integration: REST APIs, WebSocket, SSE.

• Databases and caching: PostgreSQL (RDS), Redis, pgvector.

• Cloud and infrastructure: AWS, EKS, containers, CI/CD.

• Monitoring and logging: ELK.

Benefits:

• Work arrangement: Fully remote position based in India.

• Employment type: Full-time opportunity.

• Technical ownership: Opportunity to take end-to-end responsibility for major GenAI components and influence architectural decisions.

• Advanced AI exposure: Work with cutting-edge technologies, including LLMs, multi-agent systems, LangGraph, RAG, and intelligent AI assistants.

• Professional development: Strengthen expertise in production-grade Generative AI, cloud infrastructure, and enterprise-scale software engineering.

• Leadership opportunities: Lead technical discussions, establish engineering standards, and mentor junior software engineers.

• Cross-functional collaboration: Work alongside experienced professionals across GenAI engineering, platform development, QA, and product teams.

• Industry impact: Contribute to innovative AI solutions transforming how energy customers understand consumption, manage bills, and optimize energy savings.

• International exposure: Opportunity to contribute to AI-powered products serving utility providers and customers across global markets.

• Collaborative culture: Join an engineering environment focused on innovation, technical excellence, continuous learning, and teamwork.

• Salary and additional benefits: Specific compensation, healthcare coverage, and other employee benefits have not been disclosed and may be discussed during the recruitment process.

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