Artificial Intelligence Engineer

People Impact is hiring an Artificial Intelligence Engineer for a Manager / Team Lead – Agentic AI & ML role in Bangalore, Karnataka, India. This hybrid position leads AI Engineers and Data Scientists building enterprise-grade GenAI, agentic AI, LLM, RAG and machine-learning solutions. The posted salary is ₹ 25-60 Lacs PA.

Responsibilities

• Lead, mentor, hire and develop a multidisciplinary team; own delivery from discovery and architecture through deployment, production support and improvement. Translate business priorities into roadmaps, milestones and releases, and manage risks and dependencies with product and engineering leaders.
• Direct work on LLM applications, agent orchestration, RAG pipelines, embeddings, vector search, APIs, microservices and cloud-native services. Guide ML models for prediction, classification, recommendations, anomaly detection, forecasting and optimization.
• Establish practices for experimentation, feature engineering, validation, evaluation, explainability, MLOps, monitoring and responsible AI. Review designs, code and models for scalability, security, reliability, performance, maintainability and cost. Promote reusable frameworks and templates, oversee production issues, and track delivery, adoption, quality, latency and business-impact KPIs.

Required qualifications

• At least 15 years across software engineering, AI/ML engineering, data science, solution engineering or technology delivery, including 6+ years delivering or leading enterprise or product AI/ML/GenAI/LLM solutions. The listing also states an experience range of 15–21 years. Proven multidisciplinary team leadership, technical coaching and stakeholder communication are required.
• Strong Python, backend engineering, ML, API-first architecture, microservices, distributed systems and cloud-native development skills. Hands-on or architecture-level experience with enterprise LLM platforms such as Azure OpenAI, Azure AI Studio / AI Foundry, Semantic Kernel or LangChain; RAG and retrieval technologies; and ML development through production readiness.
• MLOps and secure deployment experience, including CI/CD, versioning, monitoring, drift detection and retraining. Familiarity with relevant Azure services, Databricks, MLflow, FastAPI, Docker and Kubernetes; agentic patterns including MCP, A2A, tool calling and context management; observability tools; and integration with REST APIs and enterprise systems.

Preferred qualifications include multi-agent automation, GenAI safety and evaluation, reusable AI accelerators, hybrid ML/GenAI use cases, enterprise platforms such as SAP or ServiceNow, AI cost optimization, Build-Own-Operate delivery and responsible-AI governance.