AI Engineering Lead

• Compensation: USD 200,000 - USD 300,000 - yearly

Vichara is a Financial Services focused products and services firm headquartered in NY and building systems for some of the largest i-banks and hedge funds in the world.

Key Responsibilities

Architect, design, and lead multi-agent LLM systems using Lang Graph, Lang Chain, and Promptfoo for prompt lifecycle management and benchmarking.

Build Retrieval-Augmented Generation (RAG) pipelines leveraging hybrid vector search (dense + keyword) using Lance

DB, Pinecone, or Elasticsearch
.

Define system workflows for summarization, query routing, retrieval, and response generation, ensuring minimal latency and high precision.

Develop RAG evaluation frameworks combining retrieval precision/recall, hallucination detection, and latency metrics — aligned with analyst and business use cases.

Integrate GPT-4o, PaLM 2, and open-weight models (LLaMA, Mistral) for task-specific contextual Q&A.

Fine-tune transformer models (BERT, Sentence Transformers) for document classification, summarization, and sentiment analysis.

Manage prompt routing and variant testing using Promptfoo or equivalent tools.

Implement multi-agent architectures with modular flows — enabling task-specific agents for summarization, retrieval, classification, and reasoning.

Design fallback and recovery behaviors to ensure robustness in production.

Employ Lang Graph for parallel and stateful agent orchestration, error recovery, and deterministic flow control.

Architect ingestion pipelines for structured and unstructured data — including financial statements, filings, and PDF documents.

Leverage MongoDB for metadata storage and Redis Streams for async task execution and caching.

Implement vector-based search and retrieval layers for high-throughput and low-latency AI systems.

🔹
Observability & Production Deployment

Deploy end-to-end AI systems on AWS EKS / Azure Kubernetes Service
, integrated with CI/CD pipelines (Azure Dev Ops).

Build comprehensive monitoring dashboards using Open Telemetry and Signoz
, tracking latency, retrieval precision, and application health.

Enforce testing and regression validation using golden datasets and structured assertion checks for all LLM responses.

Collaborate with Dev Ops, MLOps, and application development teams to integrate AI APIs with React / FastAPI
-based user interfaces.

Work with business analysts to translate credit, compliance, and customer-support requirements into actionable AI agent workflows.

Mentor a small team of GenAI developers and data engineers in RAG, embeddings, and orchestration techniques.

• Experience:
  
  5+ years as an AI or ML Engineer

Required Skills & Experience

RAG Frameworks: Lance

DB, Pinecone, Elastic Search, FAISS, MongoDB

Agentic AI: Lang Graph multi-agent orchestration, routing logic, task decomposition

Fine-Tuning: BERT / domain-specific transformer tuning, evaluation framework design

Knowledge of Reranker-based retrieval (MiniLM / Cross Encoder)

Familiarity with Prompt evaluation and scoring (BLEU, ROUGE, Faithfulness)

Domain exposure to Credit Risk, Banking, and Investment Analytics

Experience with
RAG benchmark automation and model evaluation dashboards