AI Software Engineer
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