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,Argo

CD,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