{"id":3834815,"url":"https://alion.io/job/bharti-airtel-engineering-manager-backend-engineering-ai","title":"Engineering Manager - Backend Engineering & AI","company":{"id":2659,"name":"Bharti Airtel","domain":"airtel.in","url":"https://alion.io/company/airtel","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Leadership","role_family":"Leadership","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Gurgaon, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"CI/CD","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Incident Management","optional":false},{"name":"Java","optional":false},{"name":"LLM","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Python","optional":false},{"name":"Rest API","optional":false},{"name":"Spark","optional":false},{"name":"Vertex AI","optional":false}],"status":"live","first_seen_at":"2026-11-05T20:36:30Z","employer_posted_date":null,"last_verified_at":"2026-11-05T20:36:30Z","board_verified":false,"closed_at":null,"days_open":4,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":4},"description":"Responsibilities: \nLead and develop an engineering team responsible for backend services, APIs, distributed processing and event-driven workflows supporting FMP.\nOwn end-to-end delivery and production outcomes, including architecture, implementation, automated testing, deployment, reliability and ongoing improvement.\nDrive sound architectural decisions across microservices, asynchronous processing, messaging, data persistence, API design and service integration.\nEnsure business-critical workflows are correct, resilient, auditable and secure, with appropriate controls for idempotency, retries, duplicate processing, ordering, recovery and data consistency.\nDesign for high availability, scalability, latency, throughput and predictable performance under variable workloads.\nLead the pragmatic adoption of AI-enabled capabilities, including LLM integrations, intelligent automation and agentic workflows where they deliver demonstrable business or engineering value.\nEstablish engineering standards for code quality, CI/CD, automated testing, infrastructure as code, observability, security and operational readiness.\nOwn production reliability and continuous improvement through incident management, root cause analysis, performance optimisation, resilience engineering and reduction of recurring operational issues.\nOptimise infrastructure utilisation and cloud costs without compromising business-critical SLAs or reliability.\nPartner with Product and Architecture to translate business requirements into maintainable technical solutions, balancing near-term delivery with long-term engineering health.\nReuse UDP data products, contracts and shared platform capabilities where appropriate, avoiding unnecessary duplication of platform functionality within FMP.\nBuild a strong engineering culture based on technical ownership, accountability, continuous improvement and effective collaboration across distributed teams.\nDevelop engineers and technical leads, strengthen succession and team capability, and establish predictable delivery practices.\n\nRequirements: \nTypically 10-14 years of overall software engineering experience, including substantial experience in backend engineering and distributed systems; demonstrated engineering leadership is essential.\nStrong experience with Java and/or Python, microservices, REST APIs and distributed application architectures.\nDeep understanding of concurrency, asynchronous processing, messaging, caching, fault tolerance, consistency, high availability and resilience patterns.\nStrong experience with AWS and cloud-native services, including appropriate compute, storage, messaging, container and managed database services.\nPractical experience with event-driven architecture and Kafka or equivalent messaging platforms, including schema evolution, delivery semantics, retries, replay and failure recovery.\nStrong knowledge of relational and/or NoSQL databases, data modelling, query optimisation and high-performance data access.\nExperience with system design, capacity planning, performance profiling, load testing and cost-performance optimisation.\nHands-on understanding of CI/CD, automated testing, deployment automation, infrastructure as code, security controls and production observability.\nExperience operating production services, defining service-level indicators/objectives, managing incidents and driving root-cause remediation.\nPractical exposure to AI-enabled applications, LLM integration or workflow automation; familiarity with agentic patterns and model-serving platforms is beneficial. AWS Bedrock or Vertex AI experience is an advantage, not a substitute for strong backend engineering.\nUnderstanding of data contracts, schema governance and integration with analytical data platforms such as BigQuery or Spark is beneficial.\nFamiliarity with domain-driven design, API lifecycle management, platform engineering principles and AI-assisted software development practices.\n\nLeadership and Collaboration: \nProven ability to lead, coach and grow engineering teams, establish clear accountability and improve engineering effectiveness.\nStrong architectural judgment, with the ability to challenge design proposals and balance delivery speed, maintainability, reliability, security and cost.\nAccountability for delivery commitments, engineering quality, production performance and continuous improvement.\nExperience working with Product, Architecture, Data Engineering, Security, Platform Engineering and Operations teams.\nAbility to manage dependencies and influence stakeholders across distributed teams and organisational boundaries.\nStrong communication skills, including the ability to explain technical trade-offs, risks and outcomes to senior leadership.\nDemonstrated ability to develop technical leaders, delegate effectively and build a team that can operate without excessive dependence on individual experts.\nA product and platform mindset focused on reusable capabilities, measurable outcomes and continuous improvement.\nSkills\nAWS, Java, Microservices, Python","description_format":"text","description_chars":5041,"description_truncated":false,"requirements":{"experience_years_min":10,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Artificial Intelligence","Mobile 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