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Lead backend engineer

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Onsite

Lead backend engineer

Devicemotion Technologies

Lead backend engineer

Skills

Google Cloud PlatformMongoDBDockerNodePythonGolangJavaMicroservicesPostgreSQLMySQLKafkaRedisSnowflakeMachine LearningAIAWSKubernetesOracleAzure

2 months ago

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Important information


Contract type:

Permanent

Location:

Pune, Maharashtra, India

Work mode:

Onsite

Published on:

24 August 2026

What they need


Senior Backend Developer

We are looking for a Senior Backend Developer with strong AI proficiency and 8+ years of experience in designing, developing, and scaling robust backend systems and cloud-native architectures.

The ideal candidate will have strong expertise in AI/ML integration, Python, Java, authentication and security, databases, distributed systems, and multi-cloud environments. You should be comfortable building production-grade backend systems that leverage modern AI capabilities.

Key Responsibilities

  • Design, develop, and maintain scalable, highly available, and secure backend services.
  • Design and implement cloud-native architectures across AWS, Google Cloud Platform (GCP), and Microsoft Azure.
  • Build backend systems and APIs that integrate AI/ML models and generative AI capabilities into production applications.
  • Develop AI-powered services using LLMs, embeddings, vector databases, RAG (Retrieval-Augmented Generation), and AI APIs.
  • Design and implement reliable AI workflows, including prompt management, model integration, evaluation, monitoring, and performance optimization.
  • Build secure, high-performance REST APIs and microservices to support AI and business applications.
  • Design and implement authentication and authorization mechanisms for enterprise applications and APIs.
  • Work with OAuth 2.0, OpenID Connect (OIDC), JWT, SSO, RBAC, and modern identity and access management concepts.
  • Design and optimize database architectures for scalability, reliability, consistency, and performance.
  • Troubleshoot complex application and database performance issues, including query optimization, indexing, transactions, concurrency, and connection management.
  • Collaborate with engineering, product, DevOps, and data teams to deliver reliable production systems.
  • Drive architectural decisions around AI, scalability, security, performance, and maintainability.
  • Investigate and resolve complex production, performance, reliability, and scalability challenges.

Technical Requirements

AI / Generative AI Primary Requirement

  • Strong hands-on experience with AI/ML and Generative AI technologies.
  • Experience integrating Large Language Models (LLMs) such as OpenAI, Anthropic, Gemini, or similar models into production applications.
  • Strong understanding of RAG, embeddings, vector search, prompt engineering, AI agents, and LLM orchestration.
  • Experience with vector databases and semantic search.
  • Understanding of LLM performance, latency, token usage, model selection, evaluation, and cost optimization.
  • Experience building scalable backend infrastructure for AI-powered applications and services.
  • Knowledge of AI/ML frameworks and tools such as LangChain, LlamaIndex, Hugging Face, or similar is a plus.
  • Ability to evaluate emerging AI technologies and determine how they can be effectively incorporated into production systems.

Backend Development

  • 8+ years of professional experience in backend development and software engineering.
  • Strong hands-on development experience with Python and Java.
  • Experience with Go and/or Node.js is a plus.
  • Strong understanding of microservices, distributed systems, REST APIs, event-driven architectures, and asynchronous processing.
  • Experience building high-volume, production-grade backend systems.

Cloud & Architecture

  • Strong hands-on experience with AWS, Google Cloud Platform (GCP), and Microsoft Azure.
  • Strong understanding of cloud-native architectures, scalability, availability, fault tolerance, and disaster recovery.
  • Experience with Docker, Kubernetes, CI/CD, and infrastructure-as-code is a plus.
  • Ability to design systems that scale horizontally and handle high traffic and data volumes.

Authentication & Security

  • Strong understanding of authentication, authorization, and application security.
  • Experience implementing OAuth 2.0, OpenID Connect (OIDC), JWT, SSO, RBAC, and API security.
  • Understanding of identity management, access control, token lifecycle, encryption, secrets management, and secure communication.
  • Knowledge of common web and API security vulnerabilities and security best practices.
  • Experience securing microservices and service-to-service communication.

Database & Data Technologies

  • Strong knowledge of relational and NoSQL databases.
  • Hands-on experience with databases such as PostgreSQL, MySQL, Oracle, MongoDB, DynamoDB, or similar.
  • Strong understanding of database design, data modeling, normalization, indexing, transactions, concurrency, replication, partitioning, and sharding.
  • Experience with query optimization and database performance tuning.
  • Understanding of database scalability, high availability, backup/recovery, and data consistency.
  • Experience with Redis or other caching technologies is a plus.
  • Knowledge of Snowflake and modern data warehousing/data platforms is a plus.

Nice to Have

  • Strong experience with Snowflake.
  • Experience with Go or Node.js.
  • Experience with Kafka or other distributed messaging/event-streaming platforms.
  • Experience with Kubernetes and container orchestration.
  • Experience with observability, logging, monitoring, and distributed tracing.
  • Experience building AI agents and agentic workflows.
  • Experience working with large-scale data and high-throughput systems.

What We're Looking For

We are looking for a senior engineer with AI as a core strength, combined with deep backend engineering expertise. You should be able to take ownership of AI architecture, backend services, security, databases, and cloud infrastructure, while making pragmatic technical decisions for scalable production systems.

The ideal candidate is someone who can bridge the gap between AI innovation and enterprise-grade software engineeringturning AI capabilities into secure, reliable, scalable, and production-ready solutions.

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