Company: People Impact

LinkedIn: Visit LinkedIn

Business Type: Enterprise

Company Type: Service

Business Model: B2B

Funding Stage: Private Equity

Industry: Software Development

Salary Range: ₹ 25-60 Lacs PA

Manager / Team Lead – Agentic AI & ML

Location: Bangalore

Work Mode: Hybrid

Role Overview 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.

Key Responsibilities

- 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.

Required Qualifications

- 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
- Solid 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.

Preferred Qualifications

- 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.

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