Expert AI ML engineer

Job Description Expert AI ML engineer Must Have Technical/Functional Skills Programming Expert-level Python PySpark SQL Preferred Java Scala JavaScript/TypeScript AI/ML Frameworks-PyTorch,TensorFlow,Scikit-Learn,XGBoost,LightGBM,Hugging Face,MLflow GenAI Ecosystem-LangChain,LangGraph,LlamaIndex,Semantic Kernel,CrewAI,AutoGen,OpenAI APIs,Gemini APIs,Claude APIs RAG Technologies-Vector Embeddings,Semantic Search,Hybrid Search,Knowledge Graph RAG,Agentic RAG Vector Databases:Pinecone,ChromaDB,Weaviate,FAISS,Azure AI Search Cloud Platforms Must have experience in one or more: Azure,AWS,GCP Strong preference for: Azure OpenAI,Azure AI Foundry,AWS Bedrock,Vertex AI DevOps & MLOps-Docker,Kubernetes,GitHub Actions,Jenkins,Terraform,ArgoCD,CI/CD Databases-Oracle,SQL Server,PostgreSQL,MongoDB Roles & Responsibilities Generative AI & LLM Engineering Design and implement enterprise-scale GenAI applications using OpenAI, Claude, Gemini, Llama, Mistral, and other foundation models. Build production-grade RAG architectures with vector search and semantic retrieval. Develop AI-powered applications using prompt engineering, contextual retrieval, tool calling, and memory management. Optimize LLM performance, latency, throughput, hallucination reduction, and response accuracy. Design hybrid AI architectures combining structured data, unstructured documents, APIs, and enterprise knowledge sources. Implement guardrails, responsible AI controls, content filtering, and compliance frameworks. Agentic AI & Multi-Agent Systems Build intelligent autonomous and semi-autonomous agentic systems. Develop agent workflows using: LangGraph,CrewAI,AutoGen,Semantic Kernel,MCP (Model Context Protocol),Agent-to-Agent Architectures Implement: Planning Agents,Task Decomposition Agents,Reflection Agents,Tool Use Agents,Multi-Agent Collaboration Frameworks Develop dynamic orchestration frameworks for enterprise workflows. Build human-in-the-loop validation and approval mechanisms. Retrieval Augmented Generation (RAG) Build advanced RAG pipelines for banking use cases. Implement: Hybrid Search,Semantic Search,Metadata Filtering,Re-ranking Models,Knowledge Graph RAG,Agentic RAG Develop ingestion pipelines for: PDFs,SharePoint,Confluence,Databases,APIs,Message Queues Optimize chunking, embeddings, retrieval accuracy, and respons e grounding. AI/ML Engineering Build supervised and unsupervised machine learning solutions. Design and deploy: Classification Models,Regression Models,Recommendation Systems,NLP Models,Time Series Forecasting,Anomaly Detection Models Fine-tune foundation models and open-source LLMs. Develop model evaluation and benchmarking frameworks. Data Engineering Design scalable data platforms supporting AI workloads. Build: ETL Pipelines,Real-Time Streaming Pipelines,Batch Processing Pipelines Work with: Kafka,Spark,Databricks,Airflow,Hadoop Ecosystem,Delta Lake Develop enterprise metadata and lineage solutions. Handle large-scale structured and unstructured data processing. MLOps & AI Platform Engineering Design end-to-end MLOps frameworks. Implement: Model Registry,Feature Store,Experiment Tracking,Automated Retraining,Continuous Monitoring Build CI/CD pipelines for AI applications. Enable production deployment through Kubernetes and containerized environments. Develop observability dashboards and operational runbooks. Banking Domain Responsibilities Build AI use cases supporting: Capital Markets,Investment Banking,Trading Operations,Risk Management,Treasury,Compliance,AML/KYC,Regulatory Reporting Apply AI governance standards for regulated financial environments. Ensure solutions meet banking security, audit, privacy, and compliance requirements. Salary Range- $110,000-$125,000 a year Show more Show less