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AI Engineer – FDE, Forward Deployed Engineer

• Design, build, and deploy end-to-end software workflows and automation across the entire GTM stack.
• Integrate LLM APIs, autonomous AI agents, and custom middleware into existing sales, marketing, and customer retention systems.
• Optimize data pipelines ensuring seamless flow between lead generation, intent signals, pipeline movement, and customer lifecycle data.
• Evaluate, benchmark, and implement emerging AI tools and frameworks to accelerate lead scoring, personalization, automated prospecting, and churn prediction.

GTM, AI Engineer

• Design, develop, and deploy production-ready AI infrastructure and machine learning models
• Architect Agentic AI workflows and RAG systems for enterprise use cases
• Build high-performance, scalable data pipelines to support AI/ML model training and inference
• Develop and implement solutions leveraging Generative AI, Computer Vision, and NLP
• Optimize model performance, latency, and cost for production environments
• Stay current with emerging AI/ML frameworks, tools, and best practices
• Design and develop front-end and back-end components to support production applications
• Build robust APIs and integrate AI/ML models into web and enterprise applications
• Ensure application scalability, security, and performance across the stack
• Provide ongoing production support for Production applications, including troubleshooting, root cause analysis, and issue resolution
• Monitor application health, performance, and reliability post-deployment
• Create and maintain support documentation, runbooks, and knowledge base articles
• Respond to incidents and manage escalations in a timely manner

AI Full Stack Engineer

• Develop AI-powered workflows for: BOQ Generation, Quantity Takeoff, Formwork Estimation, Shop Drawing Automation, IFC Processing, Floor Plan Analysis and Generation, Drawing Intelligence, CAD Automation, Revit Automation, AI Model Integration, Geometry Processing, Document Intelligence
• Improve: Accuracy, Performance, Automation, Engineering logic, Scalability
• Collaborate with architects and engineers to solve real-world construction problems.

Senior AEC/BIM AI Engineer

• Design, build, and deploy AI-powered applications using Large Language Models, multimodal models, and intelligent agents.
• Develop scalable AI solutions that automate enterprise workflows and enhance customer experiences.
• Build reusable AI services, APIs, and components that integrate seamlessly into Aion's platform.
• Rapidly prototype, validate, and productionize AI use cases across multiple industries.
• Develop advanced prompt engineering strategies, Retrieval-Augmented Generation (RAG) pipelines, and agentic workflows.
• Build multi-agent systems capable of reasoning, planning, and executing complex enterprise tasks.
• Integrate foundation models from leading providers while optimizing performance, reliability, and cost.
• Evaluate, fine-tune, and optimize AI models for production use cases.
• Integrate AI applications with enterprise systems, databases, APIs, and third-party platforms.
• Build scalable inference pipelines and AI workflows that support production deployments.
• Collaborate with platform and infrastructure teams to optimize model serving, orchestration, and deployment.
• Implement robust evaluation frameworks to continuously measure AI quality and performance.
• Optimize AI applications for latency, accuracy, scalability, and operational efficiency.
• Implement observability, monitoring, logging, and evaluation pipelines for AI systems.
• Ensure AI solutions meet enterprise requirements for security, privacy, governance, and compliance.
• Troubleshoot production AI systems and continuously improve model performance through experimentation and feedback.
• Write clean, maintainable, and production-quality code following engineering best practices.
• Conduct code reviews and contribute to technical design discussions.
• Collaborate closely with Product, Platform, Infrastructure, and Customer Success teams to deliver customer-centric AI solutions.
• Stay current with emerging AI research, tools, and technologies, and evaluate their applicability to Aion's platform.

• Design, develop, and maintain the software solution (Studio) with drag-drop flow builder and execution engine that integrates multiple channels like voice, SMS, and chatbot with the Five9 contact center voice solution
• Modernize and re-architect the existing Studio product to use Java microservices on GCP
• Software design and development, testing and documentation across backend, front-end, and interaction management platforms to deliver end-to-end features
• Work with product owners and designers to expand the feature set and create high-quality user interfaces
• Take operational responsibility for the performance and quality of services owned by your team
• Participate in an on-call rotation and deep dive into cutting edge technologies
• Collaborate with the products team to transfer ideas into products

Senior Software Engineer – AI

• Design and engineer software with customer/user experience as primary objective
• Define, create, and provide oversight of engineering and design solutions
• Collaborate with various feature teams to produce high-quality software
• Work within a platform or feature team to engineer complex software

Principal Engineer, AI

• Develop and implement end-to-end AI-driven applications, integrating generative AI models
• Build and maintain scalable front-end interfaces using modern web frameworks (React, Angular, Vue.js)
• Design and develop robust back-end services using Node.js, Python, or other back-end technologies
• Integrate large language models (LLMs) and multimodal AI models into applications
• Optimize application performance, ensuring efficiency in AI inference and API response times
• Develop and maintain RESTful and GraphQL APIs for AI model interaction
• Deploy AI applications on cloud platforms (AWS, GCP, Azure) using containerization and serverless technologies
• Ensure security, scalability, and compliance of AI-driven applications
• Stay up to date with the latest advancements in Generative/Agentic4 AI, web development, and cloud technologies

Full Stack AI Engineer

• Building secure, AI search and retrieval for large‑scale enterprise document platforms using Azure AI Search and Azure OpenAI.
• Experience with large‑scale enterprise document repositories.
• Strong understanding of Retrieval‑Augmented Generation (RAG), Vector search, and experience grounding LLM responses in enterprise data sources.
• Index design for unstructured enterprise documents using hybrid search (keyword + vector).
• Designing prompts that constrain responses to source documents, reduce hallucinations, and support citation‑based answers.
• Strong backend engineering experience (Java, Python).
• Experience building APIs or services that orchestrate Search, Retrieval, LLM calls.
• Implement security-aware retrieval, including Document‑level access control.
• Experience measuring and improving retrieval relevance, answer accuracy, and latency across search and generation.
• Integrating AI search into internal applications.

AI/ML Engineer

• Lead the design, development, and ownership of AI-powered internal platforms and systems, including initiatives built from the ground up.
• Own end-to-end delivery of complex projects, from problem discovery and system design through production rollout and iteration.
• Design and implement AI-driven features, intelligent automation, and orchestration systems that enhance customer experiences and compound productivity across teams.
• Embed AI capabilities into customer-facing products, internal tools, and operational workflows, ensuring solutions integrate naturally and deliver measurable value.
• Build full-stack systems spanning backend services, APIs, data layers, and frontend interfaces.
• Translate ambiguous business and operational problems into scalable, maintainable, AI-enabled technical solutions.
• Apply strong systems thinking to ensure reliability, performance, security, permissions, and auditability across platforms.
• Collaborate closely with engineers, teams, and stakeholders across the company to identify high-impact opportunities and drive adoption.

AI Engineer II

• Develop and implement Machine Learning and Generative AI solutions to solve business challenges.
• Build, test, and optimize AI models using Python and modern AI/ML frameworks.
• Design and develop Retrieval-Augmented Generation (RAG) applications using enterprise knowledge sources.
• Assist in developing AI Agents and multi-agent workflows using frameworks such as LangChain, LangGraph, CrewAI, or similar technologies.
• Perform data preparation, feature engineering, model training, evaluation, and performance monitoring.
• Support deployment and operationalization of AI models using MLOps best practices.
• Integrate AI solutions with enterprise applications, APIs, databases, and cloud platforms.
• Collaborate with business stakeholders, developers, and data teams to gather requirements and deliver AI-driven solutions.
• Conduct research on emerging AI technologies, tools, and frameworks and recommend innovative solutions.
• Create technical documentation, test cases, and deployment artifacts for developed solutions.
• Participate in code reviews, knowledge-sharing sessions, and continuous improvement initiatives.

Associate AI-ML Engineer

• Build & support automation requirement & opportunities across analytics programs
• Build Power BI dashboards, semantic models, KPI scorecards, executive reports
• Partner with BU stakeholders to identify AI use cases and deliver intelligent knowledge discovery capabilities.
• Design and develop monitoring and QA automation solutions to streamline processes, reduce manual effort, and improve operational efficiency.
• Work with stakeholders in providing additional analysis based on specific needs
• Support and contribute to various GenAI initiatives across the organization.
• Research and stay UpToDate with the latest advancements in generative AI technologies, models, and frameworks.

Senior Analyst – AI Engineer

• Design, build, and deploy scalable Artificial Intelligence and Machine Learning solutions for real-world business problems.
• Develop, train, evaluate, and optimize machine learning models using structured and unstructured data.
• Build robust data pipelines for feature engineering, model training, validation, and inference.
• Deploy ML models into production environments and monitor performance, scalability, and reliability.
• Collaborate with engineering teams to integrate AI capabilities into enterprise applications and APIs.
• Evaluate new AI techniques, frameworks, and technologies to improve solution effectiveness.
• Implement model monitoring, retraining strategies, and MLOps best practices for production systems.
• Work closely with product managers and business stakeholders to translate business requirements into AI-driven solutions.
• Mentor junior engineers by conducting code reviews and sharing best practices in AI and software engineering.
• Ensure compliance with security, governance, and responsible AI principles throughout the development lifecycle.

Senior AI/ML Engineer

• Translate ambiguous stakeholder requirements into structured solutions, user flows, and deliverables
• Design system architecture, making decisions around scalability, maintainability, and security
• Build and deliver end-to-end features across frontend and backend with full ownership
• Rapidly prototype AI-enabled solutions and evolve them into production-ready systems
• Improve and scale an existing Python-based application, addressing backlog, performance, and UX gaps
• Define and implement UX flows and usability improvements (not visual design, but user journeys)
• Integrate with APIs, including AI-driven services, and design robust data flows
• Apply secure development practices and ensure systems are reliable and maintainable
• Balance speed of delivery with long-term engineering quality and structure
• Contribute to and elevate engineering practices (architecture, SDLC, testing) through hands-on work
• Collaborate with stakeholders and team members while operating with a high degree of independence

Senior Full Stack AI Engineer, Contract

• Own and define enterprise‑scale AIOps, GenAI, and Agentic AI platform strategy across multiple clients/accounts
• Act as final technical authority for architecture, design reviews, risk mitigation, and production readiness
• Lead operations and governance , including CAB ownership, platform stability, SLAs, KPIs, and continuous improvement
• Drive industrialized adoption of GenAI & Agentic AI , converting PoCs into secure, scalable, production solutions delivering measurable business outcomes
• Lead, mentor, and manage multi‑location AI/Automation teams , while advising senior stakeholders and supporting presales and roadmap decisions

GenAI & Agentic AI Engineer – Lead

• Lead engineering delivery for agentic AI capabilities across GTM stakeholders and technology stacks (Salesforce, Slack, third-party apps, and in-house platforms), owning requirements through production deployment and post-release observability
• Design and build LLM-powered workflows, autonomous agents, and multi-agent systems using Agentcore, Slack, Model Context Protocols (MCPs), LangChain, and LangGraph — then ship them via automated pipelines you maintain
• Define scalable enterprise AI architecture patterns: model routing, orchestration, memory management, context-window governance, and multi-tenant isolation strategies
• Design and optimize RAG systems, semantic search pipelines, vector retrieval strategies, and enterprise knowledge-grounding frameworks for GTM data domains
• Build and maintain Salesforce Apex, Lightning Web Components, Platform Events, and Agentforce agent actions, integrating them with AI back-ends through secure, event-driven patterns
• Build and operate platform observability stacks (tracing, logging, alerting) and AI-specific metrics while managing infrastructure-as-code (Terraform / CDK) across AWS Bedrock and Vertex AI
• Implement DevSecOps and evaluation frameworks: supply-chain security, prompt benchmarking, hallucination reduction, and automated regression testing for non-deterministic outputs
• Define error handling, fallback strategies, and graceful degradation patterns for non-deterministic AI systems, including circuit-breaker patterns at both the application and infrastructure layers
• Retire legacy integrations and replace them with modern, agentic, event-driven architectures, eliminating manual toil through automation and self-healing runbooks
• Champion engineering excellence: code reviews, runbook documentation, blameless post-mortems, and capacity planning that spans both application logic and underlying compute
• Evaluate AI vendors and platforms with a strategic build-vs-buy mindset, factoring in total cost of ownership, compliance posture, and operational burden
• Identify, scope, and automate manual GTM processes to increase organizational leverage and reduce time-to-insight for go-to-market teams

Senior AI Engineer

• Lead a team of GenAI engineers — providing technical leadership, architecture guidance, mentoring, and code reviews to drive engineering excellence and delivery quality.
• Actively contribute production-quality code — this is a hands-on role; you will design, develop, and deploy GenAI solutions alongside the team, not just guide from the sidelines.
• Design and deploy production-grade GenAI and Agentic AI applications — secure, scalable, compliant, and aligned with BFSI regulatory requirements (RBI, SEBI, GDPR).
• Build AI agents and multi-agent workflows using Amazon Bedrock, Bedrock Agents, AgentCore, and modern agent orchestration frameworks — enabling autonomous, multi-step financial AI processes.
• Design and implement RAG-based solutions — including vector database integration, chunking strategies, retrieval optimisation, prompt engineering, AI guardrails, and model evaluation frameworks.
• Develop reusable frameworks, libraries, and accelerators — standardising GenAI engineering practices and improving delivery velocity across the team.
• Design and implement cloud-native solutions on AWS — using services such as Bedrock, SageMaker, Lambda, S3, and Infrastructure as Code (Terraform) for scalable, production-ready deployments.
• Collaborate with Solution Architects, Product Owners, and business stakeholders — translating complex BFSI requirements (fraud detection, risk analytics, compliance, customer engagement) into scalable GenAI solutions.
• Troubleshoot complex technical issues — from hallucination rates and retrieval quality to latency, cost optimisation, and production incidents — guiding teams through design and implementation challenges.
• Produce high-quality technical documentation — including architecture documents, HLDs, LLDs, API documentation, deployment guides, and operational runbooks for audit and knowledge sharing.
• Stay current with emerging AI technologies — evaluating new models, frameworks, and approaches; recommending improvements to architecture, engineering practices, and solution design.

Senior/Lead GenAi Engineer

• Design, develop, and maintain AI-enabled applications and services using modern software engineering practices.
• Build and optimize data pipelines and machine learning workflows that improve performance, reliability, and usability.
• Collaborate with product and engineering partners to translate requirements into clear technical solutions.
• Write clean, testable code and help establish engineering standards for performance, security, and maintainability.
• Monitor deployed systems, troubleshoot issues, and iterate based on real-world feedback and metrics.

Full Stack AI Developer

• Build customer-facing and internal product features end to end.
• Develop web flows, backend APIs, admin tools, dashboards and operational systems.
• Support products across insurance, payments, savings, investing, travel and financial services.
• Work with product, design, mobile and operations teams to launch fast.
• Integrate APIs, handle data flows and improve production reliability.
• Use analytics, feedback and production issues to improve features after launch.
• Help build tools that reduce manual work and improve speed across the business.

Full Stack Software Engineer – AI Neobank App

• Architect multi-cloud AI solutions leveraging Azure AI, Azure OpenAI, AWS Bedrock, serverless components, microservices, and AI agents
• Design end-to-end AI-enabled IAM workflows integrating identity data, LLMs, RAG pipelines, vector databases, and IAM toolchains
• Lead Python-based microservices development (FastAPI/Flask) for IAM automation and API-driven integrations
• Design AI orchestration using LangChain, LangGraph, AutoGen, LlamaIndex, and Semantic Kernel
• Build autonomous multi-agent systems with reasoning and decision intelligence
• Lead client workshops, PoCs, MVPs, and presales demonstrations

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