AI Engineering Lead

Key Responsibilities

Architecture & System Design

• Architect design and lead multi-agent LLM systems using LangGraph LangChain and Promptfoo for prompt lifecycle management and benchmarking.

• Build Retrieval-Augmented Generation (RAG) pipelines leveraging hybrid vector search (dense keyword) using LanceDB 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.

AI Model Integration & Fine-Tuning

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

• Fine-tune transformer models (BERT SentenceTransformers) for document classification summarization and sentiment analysis.

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

Agentic AI & Orchestration

• 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 LangGraph for parallel and stateful agent orchestration error recovery and deterministic flow control.

Data Engineering & RAG Infrastructure

• 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 DevOps).

• Build comprehensive monitoring dashboards using OpenTelemetry 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.

Cross-functional Collaboration

• Collaborate with DevOps 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.

Qualifications :

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

• Required Skills & Experience

• LLMs & GenAI: GPT-4o PaLM 2 LangGraph LangChain Promptfoo SentenceTransformers

• RAG Frameworks: LanceDB Pinecone ElasticSearch FAISS MongoDB

• Agentic AI: LangGraph multi-agent orchestration routing logic task decomposition

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

• Infra & MLOps: FastAPI Docker Kubernetes (EKS/AKS) Redis Streams Azure DevOps CI/CD

• Monitoring: OpenTelemetry Signoz Prometheus

• Languages & Tools: Python SQL REST APIs Git Pandas NumPy

• Nice-to-Have Skills

• Knowledge of Reranker-based retrieval (MiniLM / CrossEncoder)

• 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

Additional Information :

Remote Work :

Yes

Employment Type :

Full-time