Expert AI ML engineer

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