Drishya AI Labs is a deep-tech AI solutions company with offices in Bengaluru and Calgary, focused on making industrial and energy operations smarter, cleaner, and more efficient through artificial intelligence and data-driven automation.

We build SaaS and platform solutions that turn complex plant and field data into actionable insights, enabling predictive maintenance, operational optimization, and better energy outcomes.

Our culture blends rigorous engineering with rapid, product-centric iteration, and we take pride in superior design, clear communication, and dependable execution.

Role Overview :

We are looking for a hands-on Development Manager who can own delivery end-to-end : shape product and technical direction, build and scale teams, and ship reliable AI-powered products into production.

You will operate at the intersection of software engineering, data science, and industrial domain experts, taking ideas from concept to customer impact in a fast-evolving AI and energy-tech landscape.

Key Responsibilities :

- Own end-to-end delivery of product features and platforms, from requirements and architecture through implementation, testing, deployment, and post-release improvements.

- Build, lead, and grow high-performing engineering teams, setting clear goals, providing mentorship, and creating an environment of ownership and accountability.

- Architect and implement scalable backend services and data-heavy systems using NodeJS, Python, PostgreSQL, Neo4j, and modern cloud platforms (e.g., AWS, GCP, Azure).

- Design and operate cloud-native workloads on Kubernetes, setting up robust, observable, and secure services suitable for industrial environments.

- Champion CI/CD and DevOps practices using GitHub Actions, automation, and testing to achieve fast, reliable, and repeatable releases.

- Collaborate closely with data science teams to integrate data science models and AI Agents into production systems, ensuring they deliver measurable business value.

- Work with product management, domain experts, and customers to translate business problems into clear technical roadmaps and execution plans.

- Establish and refine engineering best practices, coding standards, code review culture, and quality gates to maintain a consistently high technical bar.

- Track and improve delivery KPIs (velocity, lead time, reliability, defects) and drive continuous improvement in processes and tooling.

Qualifications :

- 6 - 15 years of experience in software engineering, with several years in a technical leadership, team lead, or development management role.

- Proven track record of leading teams to deliver complex, production-grade software products or platforms, ideally in B2B, industrial, or enterprise environments.

- Strong hands-on experience with backend engineering using NodeJS and Python, including system design, microservices, and API development.

- Solid experience with PostgreSQL and at least one graph or NoSQL database (preferably Neo4j), including performance and data modeling considerations.

- Practical exposure to Kubernetes and cloud-native architectures on major cloud platforms (AWS, GCP, Azure), including deployment, scaling, and monitoring.

- Experience setting up and maintaining CI/CD pipelines (GitHub Actions or similar) and working in modern DevOps-oriented environments.

- Familiarity with data science workflows and integration of AI/ML or AI Agents into products; prior work in industrial, energy, or process industries is a strong plus.

- Strong problem-solving and decision-making skills, with the ability to manage ambiguity and make pragmatic trade-offs between speed, quality, and scope.

- Excellent communication and stakeholder management skills, comfortable working with cross-functional teams and senior leadership.

- Bachelors degree in Computer Science, Engineering, or a related field; a Masters degree or MBA is a plus.

What Success Looks Like in 612 Months :

- A stable, high-performing team is in place, with clear ownership across services and modules, and a visible culture of accountability, code quality, and continuous improvement.

- Key product components are running reliably in production on cloud and Kubernetes, with mature CI/CD pipelines and monitoring in place for core services and AI-driven features.

- At least one major end-to-end capability that uses data science or AI Agents is shipped and adopted by customers, demonstrating clear value in terms of efficiency, uptime, or energy outcomes.

- Delivery metrics (lead time, release frequency, defect rates) are trending in the right direction, and the team can consistently hit committed milestones without unsustainable heroics.

- Strong collaboration patterns are established with product, data science, and domain experts, leading to a predictable roadmap and a shared understanding of priorities and trade-offs.