AI Engineer

• Ship and debug code on a live, real-time voice pipeline where latency and correctness are user-facing
• Design control systems around LLMs: guardrails, budgets, watchdogs, safe fallbacks
• Build and operate LLM evaluation and batch-analysis pipelines
• Own traditional ML workflows from data to scheduled production inference
• Trace production issues from a metric anomaly to root cause, including building the evidence when the cause is a vendor

Senior Software Engineer – Applied AI

• Design, build, and deliver AI-powered solutions across a range of engagement types — from sustained strategic initiatives requiring deep discovery and architecture work, to rapid field engagements focused on integrating LOB workflows with enterprise AI platforms and validating feasibility through working prototypes.
• Leverage enterprise AI capabilities include large language models, agentic workflows, cloud computing services (AWS/Azure), and container orchestration (Kubernetes) to solve complex, real-world business problems with a high degree of accuracy and speed.
• Lead application and solution design in partnership with architects, product owners, and LOB technical leads — translating ambiguous business problems into technically sound, implementable solutions with clear production intent from kickoff.
• Own delivery end to end — from scoping and design through development, handoff, and knowledge transfer — maintaining high standards for code quality and solution design at every stage.
• Actively engage with LOB stakeholders to understand not just the problem at hand, but adjacent challenges and emerging needs — surfacing field intelligence that informs the broader AI product roadmap and strategic investment decisions.
• Provide technical guidance and coaching to junior engineers on the team, serving as an informal technical lead within your team — setting the bar for code quality, solution design, and delivery standards without carrying formal management responsibility.
• Demonstrate subject matter expertise across multiple technology disciplines, serving as a credible technical resource for both LOB partners and internal engineering teams.
• Perform analysis, assessment and resolution for defects and incidents of advanced complexity and escalate appropriately.

Software Engineer II – Enterprise AI Products

• Design and develop interactive AI native front-end applications powered by AI Agents/LLMs
• Create intuitive, AI-native user experiences that enable seamless interaction, collaboration, and control across Agentic AI systems and autonomous workflows
• Partner with AI engineering / Data Science teams to integrate APIs, event driven (SSE),polling, webhooks and/or similar features
• Prototype, design and implement modern Agentic UX standards, protocols and ideas , Ex. Elicitation, UX negotiation and A2UI compliant Apps
• Build full stack AI Agents with Google ADK implementing advanced features like A2A, AAIF, A2UI, Agent skills, and MCP
• Research and adopt modern full-stack technologies, Agentic UX innovations, and front-end best practices
• Perform testing, debugging, optimization, and performance tuning to ensure high-quality user experiences
• Partner with product managers, UX designers, and AI architects to translate business requirements into production-ready solutions

AI Engineer – Full Stack

• Design, develop, and deploy machine learning models and AI-powered applications
• Collaborate with cross-functional teams to identify and implement AI/ML solutions for client needs
• Build and maintain data pipelines, model training workflows, and inference systems
• Evaluate and integrate large language models (LLMs), generative AI tools, and emerging AI frameworks
• Monitor model performance, conduct testing, and iterate on improvements
• Document technical architectures, methodologies, and best practices
• Stay current with AI/ML research and translate advancements into practical applications

• Build and maintain shared infrastructure: secure web app hosting infrastructure, MCP/API standards, secret/key management, model selection, guardrails, and a centralized pattern library that all builders across the organization can pull from.
• Own the review, tracking, and advisory process for all corporate AI and automation builds — MCP servers and tool configurations, Claude Skills, API integrations, and agentic workflows — before deployment.
• Partner directly with AI specialists across departments to identify, scope, and build AI and automation solutions that meet high standards for cost, security, and scalability security before they ship.
• Define and maintain the secure intake path for AI and automation requests across the organization, replacing ad-hoc builds and shadow IT with a governed, cost-effective, and scalable process.
• Contribute to the organization’s AI cost efficiency by monitoring, alerting, and setting thresholds on AI spend across usage-based tools and platforms.

AI & Automations Lead

• Design and implement scalable AI architectures, including: LLM-powered applications, Retrieval-Augmented Generation (RAG) systems, agentic / multi-step workflows, vector search and retrieval services, model serving and inference layers.
• Establish reusable platform services, APIs, and design patterns to accelerate delivery across multiple teams.
• Build and operationalize AI delivery pipelines: CI/CD for models, prompts, and workflows, prompt versioning and lifecycle management, evaluation and testing frameworks, model and artifact registries.
• Implement monitoring for response quality and hallucination control, latency, throughput, and system reliability, cost observability and optimization.
• Design AI systems with strong controls for data security and privacy auditability and traceability, entitlements and access controls, data lineage and governance.
• Lead delivery of production-grade AI systems with a focus on scalability and reliability, latency and performance optimization, operational readiness and support.
• Partner closely with data engineering and platform teams to integrate AI capabilities with Snowflake and Databricks environments, structured and unstructured data pipelines, APIs and enterprise data services, semantic and knowledge-layer architectures.

Director, AI Engineering – Agentic Platform

• Design and build multi-agent orchestration patterns (master-orchestrator + specialized worker agents) using protocols such as MCP and A2A
• Lead end-to-end delivery of agentic underwriting and claims automations, from prototype through production hardening
• Partner with IE on orchestration entry-point design (e.g., Azure Function endpoints, master-agent gateways) so the AE/IE seam is clean and scalable
• Lead offshore engineers and team members on agentic patterns, prompt engineering, and reliability practices
• Establish coding, evaluation, and observability standards for agentic systems within the AI & Automation Center of Excellence
• Translate business intent into working agentic systems — not just systems that compile, but systems that deliver measurable business outcomes.

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