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.