Engineering Lead – AI & Developer Platform
New
IndiaFull-TimeLead
Salary not disclosed
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Job Details
- Experience
- 6–10+ years
- Required Skills
- Node.jsPythonJavaKubernetesGoPrometheusCI/CDTerraform
Requirements
- 6–10+ years of software engineering experience with strong exposure to platform engineering, distributed systems, and scalable architectures.
- Proven experience building or contributing to internal developer platforms or large-scale engineering ecosystems.
- Strong proficiency in backend development using Java, Python, Node.js, or Go.
- Hands-on experience integrating AI/LLM technologies (OpenAI, Anthropic, Gemini, or similar) into products or workflows.
- Strong understanding of system design, APIs, microservices, and event-driven architectures.
- Experience with CI/CD tooling (GitHub Actions, Jenkins), infrastructure-as-code (Terraform, Helm), and cloud platforms (AWS, GCP, or OCI).
- Strong knowledge of observability tools such as Prometheus, Grafana, OpenTelemetry, Loki, or ClickHouse.
- Familiarity with AI concepts such as embeddings, prompt engineering, vector databases, and RAG pipelines.
- Experience with Kubernetes and containerized environments at scale.
- Strong leadership, communication, and stakeholder management skills across cross-functional teams.
- Experience mentoring engineers and leading technical strategy in fast-moving environments.
Responsibilities
- Lead the architecture and delivery of AI-powered platform initiatives, ensuring scalable and production-grade implementation across engineering ecosystems.
- Design and build internal developer platform capabilities that improve engineering productivity, automation, and system reliability.
- Drive AI integration across customer-facing products, internal tools, workflows, and support systems using modern LLM and agentic frameworks.
- Define and enforce engineering standards, reusable frameworks, and best practices across platform and application teams.
- Partner with infrastructure, SRE, security, and product teams to ensure scalable, secure, and highly available platform systems.
- Improve CI/CD pipelines, observability, logging, monitoring, and operational excellence across distributed systems.
- Evaluate and implement modern AI tools, including LLMs, RAG pipelines, vector databases, and AI copilots for engineering use cases.
- Mentor engineers and provide technical leadership across architecture decisions and modernization initiatives.
- Guide long-term technical strategy for platform evolution, developer experience, and AI-driven engineering transformation.
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