Manager AI

Location: Bangalore (Hybrid)ROLE SUMMARYAs Manager – AI Engineering, you will lead a multidisciplinary team of AI Engineers andData Scientists delivering GenAI-powered solutions, agentic AI systems, LLM-integratedapplications, machine learning models, and retrieval-enabled intelligent products.

Thisrole combines technical leadership, people management, delivery accountability, andproduct ownership. You will be responsible for ensuring that the team delivers scalable,secure, supportable, and business-relevant AI and ML solutions from discovery throughproduction operation and continuous improvement.This is not a lightweight line-management role. The successful candidate must betechnically strong enough to guide senior engineers, data scientists, and technicalleads, make sound architectural and implementation trade-offs, and raise theengineering bar of the team. You are expected to provide hands-on technical directionacross LLM-powered systems, agent orchestration, RAG, ML model development,MLOps, cloud-native application delivery, integrations, observability, and SDLCdiscipline, while also building a culture of accountability, ownership, experimentationrigor, and engineering excellence.You will work closely with Product Managers, Solution Architects, Lead AI Engineers,Lead Data Scientists, Platform teams, DevOps, Security, and business stakeholders totranslate business goals into implementable engineering work and durable productoutcomes.KEY RESPONSIBILITIES● Lead and mentor a team of AI Engineers and Data Scientists across varyingexperience levels, including senior engineers and technical leads, ensuring strongdelivery quality, modeling rigor, engineering discipline, and technical growth● Own end-to-end delivery of GenAI, agentic AI, and ML-enabled solutions — fromdiscovery, design, and backlog shaping through development, testing, deployment,monitoring, and continuous improvement● Guide architectural and design decisions across LLM-powered systems,embeddings pipelines, retrieval-augmented generation (RAG), agent orchestration,tool-enabled workflows, ML model pipelines, APIs, and cloud-native applicationservices● Oversee the design, development, evaluation, and operationalization of machinelearning models for predictive, classification, recommendation, anomaly detection,forecasting, optimization, or other business use cases where applicable● Ensure strong practices across feature engineering, experimentation, validation,model performance assessment, explainability, drift awareness, and MLOpsreadiness● Translate business and product priorities into executable engineering and datascience plans, technical workstreams, model delivery milestones, and sustainablerelease outcomes● Review and challenge implementation choices to ensure systems and models arescalable, secure, observable, cost-aware, and maintainable● Partner with Product Managers and engineering leadership to prioritize work,manage technical dependencies, and align delivery with roadmap goals● Establish strong SDLC and build-own-operate practices within the team, includingdesign reviews, code reviews, model reviews, automated testing, release readiness,production support, reliability improvement, and technical debt management● Drive reuse and productivity by scaling frameworks, shared components, prompttemplates, orchestration patterns, feature templates, modeling utilities, evaluationframeworks, and internal accelerators across the team● Promote a culture of responsible AI and operational excellence, emphasizingsecurity, token and cost governance, model safety, quality, observability,reproducibility, and supportability● Coordinate with cloud platform, DevOps, Security, Integration, Architecture, and datateams to ensure enterprise readiness of all deployments● Own hiring, onboarding, coaching, performance development, and growth plans forboth the AI Engineering and Data Science team members● Track and communicate KPIs related to delivery, GenAI adoption, model quality,latency, business impact, reliability, experimentation outcomes, and productionperformance● Act as the primary technical and delivery escalation point for the team, helpingremove blockers and resolve design, modeling, execution, and operational issuesRequired Qualifications● 10+ years of experience in software engineering, AI / ML engineering, data science,solution engineering, or technology delivery, including strong experience buildingand operating production-grade intelligent systems● 4 to 5 + years of experience delivering or leading AI / ML / GenAI / LLM-poweredsolutions in enterprise or product environments● Proven experience leading multidisciplinary teams or technical pods deliveringLLM-powered products, agentic AI workflows, machine learning models, orAI-enabled application capabilities, with accountability for both technical quality anddelivery outcomes● Strong technical depth in Python, modern backend engineering, machine learningsolution delivery, API-first architectures, microservices, distributed systems, andcloud-native application delivery● Strong hands-on or design-level experience with LLM platforms and orchestrationframeworks such as Azure OpenAI, Azure AI Studio, Semantic Kernel, LangChain,AutoGen, or equivalent platforms used for enterprise GenAI delivery● Strong experience designing or guiding implementations involvingretrieval-augmented generation (RAG), embeddings pipelines, vector search,grounding strategies, and retrieval optimization using platforms such as Azure AISearch, Pinecone, Weaviate, FAISS, or equivalent● Strong understanding of machine learning model development, including featureengineering, model training, validation, tuning, evaluation, performanceinterpretation, and production-readiness considerations● Practical experience guiding or reviewing MLOps practices, including experimenttracking, model versioning, deployment automation, CI/CD for ML, monitoring, driftdetection, retraining readiness, and reproducibility● Experience building and deploying AI- and ML-enabled cloud-native services usingtechnologies such as Azure Functions, Azure Container Apps, FastAPI, Docker,Azure DevOps, GitHub, GitHub Actions, Kubernetes / AKS, Azure MachineLearning, Databricks, MLflow, or equivalent engineering and deployment platforms● Strong understanding of CI/CD, containerization, deployment automation, securedelivery practices, and operational readiness for AI-driven and ML-enabled systems● Knowledge of Model Context Protocol (MCP), agent-to-agent (A2A) interactionmodels, memory / context management approaches, and other distributed AIcoordination patterns● Practical experience with observability and operational tooling such as ApplicationInsights, Azure Monitor, OpenTelemetry, Log Analytics, Datadog, New Relic, orequivalent platforms, including monitoring of reliability, latency, cost, runtimebehavior, and model / workflow health● Strong understanding of agentic AI implementation patterns, including multi-steporchestration, tool calling, context management, and workflow decomposition● Experience integrating AI- and ML-enabled solutions with REST APIs, enterprisesystems, workflow platforms, event-driven services, or downstream businessapplications● Demonstrated ability to translate business and product needs into scalable, secure,and maintainable AI / ML engineering solutions, while guiding teams onimplementation trade-offs, experimentation choices, and delivery sequencing● Strong SDLC ownership mindset across design, build, testing, deployment, support,reliability improvement, model lifecycle management, and long-term maintainability● Proven ability to raise engineering and data science quality through code reviews,model reviews, design guidance, architectural mentoring, coaching of seniorengineers and data scientists, and reinforcement of reusable patterns and standards● Strong people leadership capability, including coaching, feedback, performancemanagement, capability development, and fostering accountability and engineeringexcellence● Strong collaboration and communication skills, with the ability to work effectivelyacross engineering, data science, product, platform, architecture, DevOps, andbusiness stakeholdersPreferred Qualifications● Experience leading implementations involving agentic AI workflows, multi-agentcoordination, tool-enabled automation, reusable orchestration abstractions, orstructured task delegation patterns● Experience with AI observability, prompt safety, runtime guardrails, hallucinationmitigation, evaluation frameworks, quality monitoring, and enterprise governancepractices for GenAI systems● Familiarity with broader enterprise AI and ML platforms such as Microsoft AIFoundry, Azure Machine Learning, PromptFlow, MLflow, Databricks, or equivalent AI/ ML lifecycle and experimentation ecosystems● Experience leading or supporting teams working on forecasting, optimization,recommender systems, anomaly detection, classification, NLP, or hybrid ML + GenAIsolutions● Experience contributing to reusable GenAI accelerators, internal SDKs,orchestration templates, prompt frameworks, feature templates, evaluation patterns,modeling utilities, or shared engineering utilities● Familiarity with enterprise integration landscapes involving SAP, ServiceNow, APImanagement layers, workflow systems, event buses, and business processplatforms● Experience with cost-aware AI and ML delivery, including token usage visibility,model selection trade-offs, compute efficiency, scaling considerations, andengineering productivity optimization● Ability to communicate complex technical and analytical decisions clearly to bothengineers and non-technical stakeholders, and to represent team directionconfidently in leadership forums● Experience operating in a build-own-operate product environment● Knowledge of responsible AI, AI quality engineering, governance-by-design, modelrisk awareness, and compliance-aware delivery in enterprise environments