Nexxa.ai

AI Engineer

• Own and deliver AI‑driven product features end‑to‑end, from problem definition to production rollout
• Design AI systems that are deeply grounded in company data, using large‑scale datasets to create rich context for models
• Partner with data engineering and analytics teams to:
• - Leverage data warehouses and lake house architectures (e.g., Snowflake, BigQuery, Redshift, Databricks)
• - Define data models and pipelines that power AI use cases
• - Apply data science best practices to understand user behavior, system performance, and model impact
• Build and maintain data‑driven context layers for AI systems, such as:
• - Retrieval and ranking pipelines
• - Feature stores or embedding indices
• - Aggregations and transformations over large datasets
• Translate raw, messy, large‑scale data into clean, structured inputs that improve model quality and reliability
• Make pragmatic tradeoffs across data freshness, model performance, latency, and cost
• Define evaluation, monitoring, and feedback loops using both offline analysis and production metrics
• Collaborate closely with product managers to align AI capabilities with user needs and business goals
• Iterate quickly: ship, measure impact, refine models and data pipelines, and repeat

Senior AI Architect

Solutions Engineer

• Decompose customer requirements from workshops and discovery into structured product requirements, system architecture, and technical specifications
• Design end-to-end solution architecture for each customer challenge, scope into a milestone driven project plan, mapped to product roadmap
• Map customer requirements to product capabilities — identifying what's reusable, what's custom, and what should become platform features
• Create technical scoping documents with effort estimates, dependencies, risks, and milestone definitions for every engagement
• Coordinate with engineers (Applied AI and Core engg.) to ensure architecture-driven execution
• Participate in customer workshops and technical deep-dives, serving as the technical authority on what's feasible, what's optimal, and what the trade-offs are
• Define integration architecture for enterprise systems: legacy ERPs, CRMs, MES, PLMs and IoT platforms
• Establish reusable architecture patterns and reference implementations that accelerate future customer deployments
• Own the technical proposal and SOW inputs — ensuring commercial commitments are technically grounded

Customer Solutions Architect

Program Manager

• Manage end-to-end project schedules for customer engagements across Applied AI (FDE team) and core engineering teams spanning multiple geographies and time zones
• Maintain real-time project status visibility — Confluence boards, Jira tracking, weekly status reports — so leadership, engineering, and the Delivery Manager always have a single source of truth
• Run internal project review cadences: bi-weekly planning reviews, customer submissions reviews, and dev question sessions across all active engagements
• Proactively identify risks, dependencies, and blockers before they become surprises — escalate to the Delivery Manager with proposed mitigations, not after deadlines slip
• Own cross-site coordination across multiple sites — bridging time zones, aligning handoffs, and ensuring nothing falls between teams
• Drive daily and weekly status updates across all active projects — post EOD updates in team channels with key changes, blockers, and next actions tagged to DRIs
• Prepare and deliver weekly internal status reports to the CPO every Friday — consolidating project health, risk register, and upcoming milestones across all accounts
• Track and maintain delivery governance artifacts: project plans, feedback/release trackers, QA checklists, go-live readiness assessments
• Coordinate resource allocation and capacity planning across FDEs and engineering — flag overload risks and propose reallocation before quality suffers
• Ensure Jira hygiene: correct assignees, updated due dates, closed tickets, and clean backlogs — so automated reporting and AI tools produce accurate outputs
• Support the Delivery Manager in preparing customer-facing materials: milestone review decks, progress summaries, and QBR data

Customer Program Manager

Engineer

• Engage directly with enterprise and strategic customers to understand their workflows, data, and technical requirements.
• Architect, build, and deploy custom solutions leveraging GenAI, LLMs, Machine Learning and Vision models, and customer data sources.
• Lead full project lifecycles: scoping, solution design, development, implementation, testing, deployment, and iteration.
• Integrate and optimize AI/ML pipelines, including data preprocessing, prompt engineering, model selection, and evaluation.
• Build reliable, scalable software integrations using APIs, cloud services, and containerized systems.
• Troubleshoot complex technical issues across the stack—applications, models, data pipelines, infrastructure, and integrations.
• Act as the customer’s trusted technical advisor, enabling adoption of new product capabilities and AI features.
• Partner closely with internal product and engineering teams to communicate customer feedback and shape roadmap direction.
• Produce high-quality documentation, architecture diagrams, runbooks, and technical assets for customer teams.
• Mentor junior engineers and contribute to internal best practices for FDE delivery.

Senior AI Architect

Customer Solutions Architect

Customer Program Manager

Forward Deployed Engineer