Senior AI Engineer

Job Description Roles & Responsibilities Designing and building Generative AI applications using Large Language Models (LLMs) · Developing Agentic AI solutions, including autonomous agents, multi-agent orchestration, and workflow-driven decision systems · Building solutions using frameworks such as LangChain, LangGraph or similar agent frameworks · Implementing RAG architecture with vector databases · Knowledge of embeddings, vector DBs, and model evaluation. · Implement various Context Engineering strategies to reduce Token Utilization & Latency. · Prompt engineering, tool calling, memory management, and agent orchestration · Integrating AI services with enterprise APIs, middle ware, and backend systems · Evaluated & Improvise AI Agent Performance using several metrics · Deploying scalable AI solutions on Azure / AWS cloud platforms Desired Candidate Profile Technical — Essential 5+ years Python development; 2+ years in production AI/ML engineering Deep LangGraph or equivalent agentic framework experience: graph topology, state management, conditional edges, checkpoint-based recovery LLM evaluation expertise: F1 measurement against ground truth, Expected Calibration Error (ECE), hallucination detection, confidence calibration Prompt engineering at scale: versioned prompts, sensitivity analysis, few-shot dataset curation, PMS governance RAG architecture: hybrid retrieval (BM25 + vector), re-ranking, chunk citation, grounding verification Azure AI Foundry or equivalent LLM inference platform: PTU, PAYG, token budget management, APIM integration Workflow orchestration: Orkes Conductor or Temporal — HITL wait tasks, retry policies, compensating transactions CI/CD pipeline integration for ML: Azure DevOps, MLflow experiment tracking, model registry promotion Technical — Advantageous Computer vision model deployment and evaluation (object detection — TPR/TNR/IoU) Azure AI Document Intelligence: form recogniser, custom models, layout analysis Kubernetes / AKS: workload identity, namespace isolation, KEDA autoscaling Apache Iceberg, DuckDB, and data contract patterns Banking domain knowledge: cheque clearing, trade finance, KYC, AML Qualifications Bachelor's degree in Computer Science, AI/ML, or equivalent; Master's preferred Azure AI Engineer Associate or equivalent certification Demonstrable production AI deployments — case studies or public work highly valued Employment Type Full Time Company Industry RecruitmentPlacement FirmExecutive Search Department / Functional Area Engineering Keywords AI Agent SpecialistReinforcement LearningAgent ArchitecturesAI Technical LeadAI Software Engineer ExpertMachine Learning Get real-time job updates only on our App