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 </div

Requirements

Must-haves

Expert AI ML engineer
Must Have Technical/Functional Skills
Must have experience in one or more: Azure,AWS,GCP

Nice-to-haves

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
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.

Application prep

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Keywords to mirror

aimlpythonsqljavajavascripttypescriptazureawsgcpdockerkubernetesci/cdllmmachine learning

Platform: careers-page
Effort: medium