Engineering Manager - Fullstack AI Technologies

Location: Mumbai, Maharashtra, IndiaSector: Technology, Information & MediaCompany Size: 500 - 1,000 EmployeesEmployment: Full-Time, PermanentExperience: 10 - 14 Years (Engineering Leadership)Level: Engineering Manager / Group EMABOUT THIS MANDATE :Recruiting Bond has been exclusively retained by one of India's most prominent and well-established digital platform organisations operating at the intersection of Technology, Information, and Media to identify and place an exceptional Engineering Manager who can lead engineering teams through an enterprise-wide AI adoption and digital transformation agenda.This is a high-impact, hands-on leadership role at the nexus of people, product, and technology. The organisation is executing one of the most ambitious AI transformation programmes in its sector and this Engineering Manager will be a core driver of that change. You will lead multiple squads, own engineering delivery end-to-end, embed AI tooling and practices into the team's DNA, and shape the engineering culture of tomorrow.We are seeking leaders who code when it matters, who build systems and teams with equal conviction, and who view AI not as a trend but as a fundamental shift in how great software is built.THE OPPORTUNITY AT A GLANCE :AI-First Engineering Culture :Own AI adoption across your squads - from LLM tooling integration to automation-first delivery workflows. Make AI a default, not an afterthought.Hands-On Engineering Leadership :Stay close to the code. Lead architecture reviews, unblock engineers, and set the technical bar - not just the management agenda.People & Org Builder :Grow engineers into leaders. Build squads of 615 across functions. Drive hiring, career frameworks, and a culture of psychological safety.KEY RESPONSIBILITIES :1. Hands-On Technical Engagement :Remain deeply embedded in the technical work participate in design reviews, architecture decisions, and critical code reviewsSet and uphold the engineering quality bar : performance benchmarks, security standards, test coverage, and release qualityProvide technical direction on backend platform strategy, API design, service decomposition, and data architectureIdentify and resolve systemic technical debt and architectural risks across team-owned servicesUnblock engineers by diving into complex problems debugging, pair programming, and system analysis when it mattersOwn key technical decisions in collaboration with Tech Leads and Principal Engineers; balance pragmatism with long-term sustainability2. AI Adoption, Integration & Transformation (2026 Mandate) :Define and execute the team's AI adoption roadmap - from developer tooling to product-facing AI featuresChampion the integration of GenAI tools (GitHub Copilot, Cursor, Claude, ChatGPT) across the full engineering workflow coding, testing, documentation, incident responseEmbed LLM-powered capabilities into the product : recommendation engines, intelligent search, conversational interfaces, content generation, and predictive systemsLead evaluation and adoption of AI-assisted SDLC practices : automated code review, AI-generated test suites, intelligent observability, and anomaly detectionPartner with Data Science and ML Platform teams to productionise ML models with robust MLOps pipelinesBuild team literacy in prompt engineering, RAG (Retrieval-Augmented Generation), and AI agent frameworksCreate an experimentation culture : run structured AI pilots, measure productivity impact, and scale what worksStay ahead of the AI tooling landscape and advise senior leadership on strategic AI investments and engineering implications3. People Leadership & Team Development :Lead, manage, and grow squads of 6 - 15 engineers across seniority levels (L2 through L6 / Junior through Staff)Conduct structured 1 : 1s, career growth conversations, and development planning with every direct reportDesign and execute personalised AI upskilling programmes ensure every engineer develops practical AI fluency by end of 2026Build and maintain a high-performance team culture : clarity of ownership, accountability, fast feedback loops, and psychological safetyDrive performance management fairly and rigorously recognise top performers, manage underperformance constructivelyLead technical hiring end-to-end : define job requirements, conduct bar-raising interviews, and make data-driven hire decisionsContribute to engineering career frameworks and level definitions in partnership with the VP / Director of Engineering4. Engineering Delivery & Execution Excellence :Own end-to-end delivery for multiple product squads from planning and scoping through production release and post-launch stabilityImplement and refine agile delivery frameworks (Scrum, Kanban, Shape Up) calibrated to squad needs and product cadenceDrive predictable delivery : maintain healthy sprint velocity, manage WIP limits, and ensure dependency resolution across teams.Establish and own engineering KPIs : DORA metrics (deployment frequency, lead time, MTTR, change failure rate), uptime SLOs, and velocity trendsLead incident management : build blameless post-mortem culture, own RCA processes, and drive systemic reliability improvementsBalance technical debt repayment with feature velocity negotiate prioritisation transparently with Product leadership5. Strategic Leadership & Cross-Functional Influence :Serve as the primary engineering partner for Product, Design, Data, and Business stakeholders translate ambiguity into executable engineering plansParticipate in quarterly roadmap planning, capacity forecasting, and OKR definition for engineering teamsRepresent engineering in leadership forums articulate technical constraints, risks, and opportunities in business termsContribute to org-wide engineering strategy : platform investments, build-vs-buy decisions, and shared infrastructure prioritiesBuild relationships across geographies (Mumbai HQ + distributed teams) to maintain alignment and delivery cohesionAct as a culture carrier and ambassador for engineering excellence, innovation, and responsible AI useAI TRANSFORMATION LEADERSHIP 2026 EXPECTATIONS :In 2026, Engineering Managers at this organisation are expected to be active architects of AI transformation not passive observers. The following outlines the specific AI leadership expectations for this role :AI Developer ProductivityDrive measurable uplift in developer velocity through AI tooling adoption. Target : 30%+ reduction in code review cycle time and 40%+ increase in test coverage automation by Q3 2026.LLM & GenAI Product FeaturesOwn delivery of GenAI-powered product capabilities : intelligent content, semantic search, personalisation, and conversational UX in production, at scale.AI-Augmented ObservabilityImplement AI-driven monitoring and anomaly detection pipelines. Reduce MTTR by leveraging predictive alerting, intelligent runbooks, and auto-remediation scripts.Team AI Fluency :Build mandatory AI literacy across all engineering levels.Every engineer understands prompt engineering basics, AI ethics guardrails, and responsible AI deployment practices.Responsible AI Governance :Partner with Security, Legal, and Data Privacy to ensure all AI deployments meet compliance standards, bias mitigation requirements, and explainability benchmarks.TECHNOLOGY STACK & DOMAIN FAMILIARITY REQUIRED :Languages: Java/ Go/ Python/ Node.js /PHP /Rust (must be hands-on in at least 2)Cloud: AWS / GCP / Azure (multi-cloud exposure strongly preferred)AI & GenAI: OpenAI / Anthropic / Gemini APIs /LangChain /LlamaIndex / RAG / Vector DBs / GitHubCopilot: Cursor /Hugging FaceContainers: Docker /Kubernetes /Helm /Service Mesh (Istio / Linkerd)Databases: PostgreSQL /MongoDB / Redis / Cassandra / Elasticsearch / Pinecone (Vector DB)Messaging: Apache Kafka /RabbitMQ /AWS SQS/SNS /Google Pub/SubMLOps & DataOps: MLflow /Kubeflow / SageMaker / Vertex AI /Airflow /dbtObservability: Datadog /Prometheus /Grafana /OpenTelemetry / Jaeger /ELK StackCI/CD & IaC: GitHub Actions ArgoCD / Jenkins / Terraform /Ansible /Backstage (IDP)QUALIFICATIONS & CANDIDATE PROFILE :Education :B.E. / B.Tech or M.E. / M.Tech from a Tier-I or Tier-II Institution - CS, IS, ECE, AI/ML streams strongly preferredDemonstrated engineering depth and leadership impact may complement institution pedigreeExperience :10 to 14 years of progressive engineering experience, with at least 3 years in a formal Engineering Manager or equivalent people-leadership roleProven track record of managing and scaling engineering teams (615+ engineers) in a fast-growing SaaS or digital product environmentHands-on backend engineering background must be able to read, write, and critique production codeDirect experience driving AI/ML feature delivery or AI tooling adoption within engineering organisationsExposure across start-up, mid-size, and large-scale product organisations, preferred adaptability is a core requirementStrong CS fundamentals: distributed systems, algorithms, system design, and software architectureDemonstrated career stability minimum of 2 years of average tenure per organisation.The Ideal Engineering Manager in 2026 :Leads with context, not control, empowers engineers while maintaining accountability and qualityIs fluent in both people language and technical language, switches registers naturally with engineers and executives alikeSees AI as a force multiplier for the team, not a threat. Actively experiments with and advocates for AI toolingMeasures success by team outcomes, not personal output. Takes pride in what the team ships, not what they build aloneCreates feedback loops obsessively between product and engineering, between seniors and juniors, between metrics and decisionsHas strong opinions, loosely held, brings conviction to discussions but updates on evidenceInvests in engineering excellence as seriously as delivery velocity knows that quality and speed are not oppositesWHY THIS ROLE STANDS APART :AI Transformation at Scale :Lead one of the most significant AI adoption programmes in India's digital media sector.Our decisions will shape how hundreds of engineers work in 2026 and beyond.Hands-On & Strategic Balance :A rare EM role that actively encourages technical depth.Stay close to the code while owning the people agenda - the best of both worlds.Established Platform, Real Scale :5001,000 engineers, proven product-market fit, and the org maturity to execute.This is not a greenfield startup gamble it is a serious company with serious ambition.Clear Leadership Growth Path :A visible, direct path toward Director / VP of Engineering.Senior leadership is invested in growing its next generation of technology executives.