Blend360

AI Engineer

• Collaborate with cross-functional teams to design and build AI-powered applications and internal platforms.
• Contribute to backend services, APIs, integrations, and frontend components when needed.
• Support the development and deployment of scalable AI and LLM-based solutions.
• Translate business and technical requirements into functional solutions.
• Rapidly prototype, test, and iterate on new ideas and AI use cases.
• Work in fast-changing environments with evolving priorities and technologies.
• Contribute to reusable engineering patterns, internal tooling, and shared components.
• Participate in code reviews, technical discussions, and collaborative problem-solving.
• Learn and adopt new frameworks, cloud technologies, and AI tools as required by the project.
• Maintain high standards for code quality, collaboration, and continuous improvement.

Fullstack AI Engineer

• Design, develop, and deploy scalable AI-powered applications and services.
• Translate ambiguous business problems into robust technical solutions.
• Build and maintain backend systems, APIs, integrations, and AI workflows.
• Collaborate across frontend, backend, cloud, and AI initiatives to deliver end-to-end solutions.
• Rapidly prototype and iterate on AI-driven products and internal accelerators.
• Contribute to architecture decisions, engineering standards, and reusable system patterns.
• Mentor junior engineers and support technical growth across the team.
• Drive improvements in scalability, reliability, performance, and developer experience.
• Work closely with stakeholders to balance business needs, technical trade-offs, and delivery timelines.
• Continuously evaluate and adopt emerging AI tools, frameworks, and engineering practices.

Senior AI Engineer

Analytics Engineer

• Implement the curated data model layer in dbt across 18 data domains, working closely with the Onshore Analytics Engineering Lead.
• Design and build dbt models across the full layered architecture: staging, intermediate, and mart layers.
• Implement comprehensive dbt testing strategies, including unique, not_null, accepted_values, relationships, and custom tests to ensure data quality and reliability.
• Develop and optimize incremental models to handle high-volume domains processing billions of events efficiently.
• Generate and maintain thorough project documentation, including column descriptions and lineage graphs, to support transparency and team collaboration.

QA Engineer

• Validate data accuracy and ensure production readiness by working closely with the Data Engineering Manager and Analytics Engineering Lead across 18 data domains.
• Design and build a reconciliation framework to systematically compare legacy pipeline outputs against new pipeline outputs, identifying discrepancies and gaps.
• Execute structured acceptance testing for each pipeline prior to promotion to production environments.
• Validate identity resolution accuracy through rigorous analysis of match rates, false positives, and false negatives.
• Document end-to-end data lineage across all 18 domains to support auditability, transparency, and regulatory compliance.
• Build and maintain automated regression test suites to enable continuous quality assurance as pipelines evolve.

Data QA Analyst

Marketing

• Act as the operational driver for a complex, multi-channel marketing campaign.
• Translate high-level plans into executable workstreams.
• Manage end-to-end execution across marketing workstreams.
• Serve as the central point of coordination across teams.
• Provide regular updates on campaign status, timelines, and risks.

Senior Marketing Manager, Campaign Execution

Full-stack Engineer

• Own architecture and delivery across multiple workstreams for Intelligent Applications.
• Define 'how we build' for forward deployed engagements—templates, standards, quality gates.
• Mentor and guide multiple engineers.
• Bring observability/security to the forefront and operationalize LLM/agent delivery into repeatable, safe workflows (evals, guardrails, monitoring, human-in-the-loop where needed).
• Lead multiple streams (UI/API/data) with clear sequencing, risks, and integration plans.
• Define reference architectures and standards: SOLID/MVC boundaries, error handling, testing, observability, security.
• Drive performance and data architecture decisions: SQL/query patterns, indexing strategy, data contracts.
• Create reusable accelerators: scaffolds, templates, spec/context packages, PR checklists, CI quality gates—including LLM/RAG reference modules and evaluation scaffolding.
• Partner with data teams when engagements lean on Databricks or Snowflake for analytics/ML adjacency (contracts, identity, performance patterns).
• Operationalize CI/CD and containerization standards: pipeline guardrails, environment parity, container-first dev workflows, and release verification patterns.
• Coach senior + mid-level engineers; improve team throughput and quality.
• Partner with leadership and clients to quantify impact and adoption.

Lead Full Stack Engineer

Solutions Engineer

• Own the practice of Solutions Architecture across a portfolio of engagements: talent, methods, risk posture, and technical strategy aligned to business outcomes—especially as clients adopt Intelligent Applications and LLM platforms.
• Set what “good solutioning” looks like—review models, accelerators, risk frameworks—and represent architecture tradeoffs to executive stakeholders.
• Contribute to highest-risk programs directly, primary accountability is scaling the capability.
• Set practice strategy: capability model, hiring bar, leveling alignment, onboarding, communities of practice.
• Establish portfolio governance: architecture review boards, exceptions, security/reliability minimums, vendor/architecture risk processes.
• Grow talent: performance coaching, sponsorship, succession planning for Staff/Principal paths.
• Define reusable assets: reference architectures, decision templates, NFR catalogs, threat modeling kits, engagement playbooks.
• Partner with sales/delivery leadership on technical win strategy, estimates risk ranges, and delivery feasibility.
• Drive LLM architecture governance with measurable controls: policy, evaluations, monitoring, incident readiness, vendor/model posture.
• Align CI/CD, observability, and platform standards across engagements with enterprise constraints.