Location: Bangalore
Work Mode: Hybrid
We are looking for an experienced Manager / Team Lead – AI Engineering to lead a multidisciplinary team of AI Engineers and Data Scientists building enterprise-grade GenAI, Agentic AI, LLM, RAG, and Machine Learning solutions.
The role combines technical leadership, people management, architecture, delivery ownership, and product engineering. The ideal candidate should be technically strong enough to guide senior engineers and data scientists, influence architecture and implementation decisions, and ensure AI/ML solutions are scalable, secure, reliable, observable, and production-ready.
You will work closely with Product Managers, Solution Architects, AI/ML Leads, Data Science, Platform, DevOps, Security, Architecture, and business teams to convert business requirements into scalable AI products and engineering solutions.
Lead, mentor, and develop a team of AI Engineers and Data Scientists, including senior engineers and technical leads.
Own end-to-end delivery of GenAI, Agentic AI, LLM, RAG, and ML solutions from discovery and design through deployment, production support, and continuous improvement.
Provide technical direction for LLM applications, agent orchestration, RAG pipelines, embeddings, vector search, APIs, microservices, and cloud-native solutions.
Guide the development and productionization of ML models for prediction, classification, recommendation, anomaly detection, forecasting, and optimization use cases.
Establish strong practices around feature engineering, experimentation, model validation, evaluation, explainability, monitoring, and MLOps.
Translate business and product priorities into technical roadmaps, engineering workstreams, delivery milestones, and release plans.
Review architecture and implementation decisions with focus on scalability, security, performance, maintainability, reliability, and cost optimization.
Partner with Product and Engineering leadership to manage priorities, dependencies, risks, and delivery commitments.
Drive strong SDLC and Build-Own-Operate practices, including design reviews, code reviews, model reviews, testing, release readiness, production support, and technical debt management.
Promote reusable GenAI frameworks, orchestration patterns, prompt templates, evaluation frameworks, modeling utilities, and engineering accelerators.
Establish responsible AI and operational excellence practices covering security, model safety, observability, cost governance, quality, reproducibility, and supportability.
Collaborate with Cloud, DevOps, Security, Integration, Architecture, and Data teams to ensure enterprise readiness.
Own hiring, onboarding, coaching, performance management, and career development for AI Engineering and Data Science team members.
Define and track KPIs covering delivery, AI adoption, model quality, latency, reliability, business impact, experimentation, and production performance.
Act as the technical and delivery escalation point for design, modeling, execution, and production issues.
15+ years of experience across software engineering, AI/ML engineering, data science, solution engineering, or technology delivery.
6+ years of experience delivering or leading AI/ML/GenAI/LLM solutions in enterprise or product environments.
Proven experience leading multidisciplinary AI/ML teams or technical pods with responsibility for technical quality and delivery.
Strong expertise in Python, backend engineering, machine learning, API-first architectures, microservices, distributed systems, and cloud-native development.
Hands-on or architecture-level experience with LLM platforms and frameworks such as:
Azure OpenAI
Azure AI Studio / AI Foundry
Semantic Kernel
LangChain
AutoGen
Equivalent enterprise GenAI platforms
Strong experience with RAG, embeddings, vector databases/search, grounding, and retrieval optimization, using technologies such as Azure AI Search, Pinecone, Weaviate, FAISS, or equivalent.
Strong understanding of ML model development, including feature engineering, training, validation, tuning, evaluation, and production readiness.
Experience with MLOps, including experiment tracking, model versioning, CI/CD, deployment automation, monitoring, drift detection, retraining, and reproducibility.
Experience deploying AI/ML services using technologies such as:
Azure Functions
Azure Container Apps
FastAPI
Docker
Azure DevOps
GitHub / GitHub Actions
Kubernetes / AKS
Azure Machine Learning
Databricks
MLflow
Strong knowledge of CI/CD, containerization, secure deployment, automation, and operational readiness.
Knowledge of MCP, A2A interaction models, memory/context management, and distributed AI coordination patterns.
Strong understanding of Agentic AI, including tool calling, multi-step workflows, context management, orchestration, and task decomposition.
Experience with observability tools such as Application Insights, Azure Monitor, OpenTelemetry, Log Analytics, Datadog, or New Relic.
Experience integrating AI/ML solutions with REST APIs, enterprise applications, workflow platforms, event-driven systems, and downstream business applications.
Strong understanding of the complete AI/ML SDLC, from design and development through deployment, monitoring, support, and lifecycle management.
Demonstrated ability to mentor senior engineers and data scientists through design reviews, code reviews, model reviews, architecture guidance, and technical coaching.
Strong people-management, stakeholder-management, communication, and problem-solving skills.
Experience leading Agentic AI, multi-agent systems, tool-enabled automation, and structured task orchestration.
Experience with AI observability, prompt safety, guardrails, hallucination mitigation, evaluation frameworks, and GenAI quality monitoring.
Familiarity with Microsoft AI Foundry, Azure ML, PromptFlow, MLflow, Databricks, or equivalent AI/ML platforms.
Experience with forecasting, optimization, recommendation systems, anomaly detection, classification, NLP, or hybrid ML + GenAI solutions.
Experience developing reusable GenAI accelerators, internal SDKs, orchestration frameworks, prompt libraries, evaluation frameworks, and ML utilities.
Exposure to enterprise ecosystems involving SAP, ServiceNow, API Management, workflow platforms, event buses, and business process systems.
Experience with AI cost optimization, including token monitoring, model selection, compute optimization, scalability, and productivity improvements.
Experience working in a Build-Own-Operate product environment.
Knowledge of Responsible AI, AI quality engineering, governance-by-design, model risk, and compliance-aware AI delivery.
Ability to communicate complex technical concepts effectively to both technical and business stakeholders and represent the team in leadership forums.