Expert AI ML engineer
Must Have Technical/Functional Skills
Programming
Expert-level:
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