Jobs at AiDash

Design, develop, deploy, and optimize scalable backend microservices and distributed systems supporting web and mobile applications. Build reliable, secure, observable services backed by relational and NoSQL databases, automated pipelines, and ML models. Collaborate with product and engineering teams, contribute to architecture, conduct code reviews, improve performance, and maintain high availability through testing, monitoring, refactoring, and security best practices.

Architect and operate the MLOps infrastructure for production model deployment, scalable inference, cost optimization, monitoring, failover, and disaster recovery. Build SageMaker-based serving and CI/CD systems with automated testing, staged rollouts, elasticity, and resilience. Establish MLOps standards as the first engineer on a new team while partnering with Data Science and Web Application teams to enable rapid, reliable model releases.

Build, test, and ship backend services and APIs in Java or Python. Work on scalable data pipelines processing satellite, sensor, and operational data; write documented, tested code; participate in reviews; debug production systems; and present an end-to-end project. The internship includes mentorship, cross-team collaboration, and potential consideration for a full-time return offer.

Leads the creation of AiDASH’s Data Inference AI Pipeline team, including hiring, architecture, engineering practices, and delivery. Designs scalable, cost-efficient multimodal AI and ML inference pipelines, training and human-feedback loops, and production AI systems. Manages engineering leaders and distributed teams while partnering with Product, Data Science, Design, and executive leadership on roadmap, architecture, resourcing, and enterprise customer delivery.

Defines AiDASH’s AI data strategy and leads data acquisition decisions across products and customers. Evaluates sourcing options, manages cost and unit-cost models, analyzes quote-to-actual variance, develops multi-year data plans, and establishes decision rules. Partners with technical, commercial, finance, legal, and customer teams to align data purchases with customer needs, costs, and model requirements. Also designs an agentic workflow to automate purchase recommendations and route exceptions for human review.

Architect and build scalable, multi-tenant SaaS backend systems from pilot through production. Design PostgreSQL schemas, APIs, tenancy isolation models, and AI-agent tool surfaces using MCP or similar protocols. Select and defend technology choices, write production code, mentor engineers, conduct design reviews, and help determine which product ideas should advance. The role requires expertise in Java, PostgreSQL, distributed systems, AI-assisted product engineering, and end-to-end product delivery.

Own quality for AI-powered platform applications by designing functional and non-functional test strategies, automating APIs with Java and RestAssured, validating data across APIs and databases, debugging services and infrastructure, conducting JMeter performance tests, maintaining CI/CD pipelines, monitoring production health, and managing defects. The role also improves agentic AI workflows for test planning, automation, execution, triage, reporting, and production validation.

Owns People & Culture strategy and execution as a business partner. Responsibilities include understanding business and workforce needs, managing performance systems, shaping compensation and rewards, supporting behavioral hiring decisions, maintaining policies and compliance across India and the UK, and using people data to guide decisions on engagement, attrition, performance, and growth.

Lead the AI Data Ops operating model by building automation roadmaps, program governance, cost-efficiency initiatives, vendor performance frameworks, SOPs, dashboards, and KPI systems. Coordinate cross-functional initiatives, manage vendor SLAs and budgets, identify operational improvements, and translate ambiguous problems into structured plans. The role requires hands-on startup experience and focuses on creating scalable, repeatable processes for imagery sourcing, processing, annotation, and quality operations.

Lead AI-augmented product design across research, prototyping, visual exploration, production-ready design, and design systems. Collaborate with cross-functional teams to create journey maps, service blueprints, roadmaps, prototypes, and scalable components. Mentor teams on AI-assisted design practices, establish standards, and manage project scope, risks, and stakeholder communication.

Develop and apply analytical models to help utility clients solve business problems, optimize operations, and realize value from AiDASH products. Collaborate with clients, engagement managers, and R&D teams to structure problems for AI solutions, generate data-driven insights, and present findings, recommendations, and business impact. The role also requires independent project delivery, innovative problem-solving, and strong communication.

Conduct high-volume outbound prospecting through cold calls, LinkedIn, and personalized email campaigns targeting electric utility decision-makers. Qualify leads, schedule meetings for Account Executives, maintain accurate Salesforce records, collaborate with sales and marketing, achieve activity targets, analyze performance metrics, and represent AiDASH at industry conferences.

Own quality assurance for AI-powered backend applications by designing functional and performance test strategies, automating APIs with Java and RestAssured, validating data across APIs and databases, and conducting JMeter performance testing. Build CI automation pipelines, monitor applications using Kubernetes and AWS, manage defects, perform root-cause analysis, and validate AI/ML outputs. Collaborate across teams to prepare test data and improve testing frameworks and reporting.

Lead enterprise sales for AiDASH’s utility-sector climate resilience platform. Originate and close complex, multi-million-dollar deals, engage C-suite utility leaders, develop account and deal strategies, expand existing customer relationships, and collaborate with Customer Success and strategic partners. The role requires approximately 30% travel and involves elevating sales excellence across the go-to-market organization.