Expert AI ML engineer

Responsibilities

• 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.
• 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.
• 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 response grounding.
• 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.
• 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.
• 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.
• 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.

Skills

• 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: Experience with 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

Pay

Salary Range: $110,000‑$125,000 per year.