Lead Software Engineer - Java, Cloud Platforms (AWS), Kafka,Generative AI

 Job Responsibilities

• Architect end-to-end AI systems and workflows (LLM-enabled features, retrieval-augmented generation (RAG), agentic patterns, decision support), defining APIs, data flows, and operational readiness.
• Lead hypothesis-driven product discovery through rapid prototyping, experimentation, and evaluation to accelerate time-to-learning and inform roadmap decisions.
• Develop secure, high-quality production code and review code written by others to uphold maintainability, performance, and resiliency standards.
• Define non-functional requirements for AI services (latency, cost, reliability, availability) and drive design decisions that meet them.
• Establish AI quality and validation standards, including offline/online metrics, human review, regression testing, and guardrails for safe operation.
• Integrate AI capabilities into enterprise applications and SDLC workflows, ensuring scalable delivery from prototype to production.
• Partner with Product, Design, Data Science/ML, and governance stakeholders to shape problem statements, success metrics, and acceptance criteria.
• Evaluate models, tools, and vendor solutions by assessing architectural fit, control requirements, and integration within existing platforms and information architecture.
• Automate remediation of recurring issues and improve operational stability through observability, incident learnings, and proactive reliability engineering.
• Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.

• Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.

  Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years applied experience.
• Formal training or certification in software engineering concepts with 10+ years of applied software engineering experience, including 3+ years delivering AI/ML and Generative AI solutions in production environments.
• Java, Cloud Platforms (AWS), Kafka, Micro services
• Hands-on experience designing and delivering LLM solutions, including RAG, embeddings, orchestration/tool calling, and prompt/model optimization.
• Demonstrated expertise building AI agents and agentic workflows (single-agent and/or multi-agent patterns) with measurable outcome tracking.
• Advanced programming capability in Python, Java, or TypeScript, with strong code quality and test discipline.
• Practical experience with AI frameworks such as LangChain, LangGraph, CrewAI, Semantic Kernel, AutoGen, or equivalent patterns/tools.
• Proven ability to define evaluation frameworks, guardrails, and validation approaches that address correctness, safety, privacy, and resiliency.
• 3+ years building and operating cloud-native production services on AWS and/or Azure (GCP experience accepted where applicable).
• Experience with MLOps/LLMOps practices, including model deployment, monitoring, observability, and lifecycle management.
• Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
• Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices

Preferred Qualifications

• Experience with vector databases, embeddings, and large-scale knowledge retrieval architectures, including RAG design patterns and performance optimization.
• Strong background in microservices and cloud-native architecture, including Docker, Kubernetes, and production-grade platform engineering practices.
• Proven use of AI-assisted SDLC practices (e.g., spec-driven development, AI-assisted code review/refactoring, test acceleration) with clear human validation and quality controls.
• Knowledge of cybersecurity controls, data sensitivity handling, and AI governance, plus experience leading enterprise modernization and influencing outcomes through technical mentoring and stakeholder management; familiarity with data engineering technologies such as Spark, Kafka, Snowflake, and/or Databricks is a plus