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