Providing innovative, comprehensive, and end-to-end information technology and workforce management solutions that empower businesses to achieve digital transformation, enhance operational efficiency, and drive sustainable growth

Job Description

We are seeking a highly skilled Applied AI Engineer to join our team in building and scaling enterprise-grade Generative AI solutions for mission-critical business applications. This role is focused on designing, developing, and operating production-ready GenAI platforms that power intelligent document processing, AI-powered assistants, automated workflows, and advanced knowledge retrieval systems. A deep expertise in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), agentic AI workflows, LLMOps, prompt engineering, AI orchestration frameworks, and AI governance.

Key Responsibilities

• Design, develop, and enhance reusable enterprise-grade GenAI workflows that support multiple business functions.
• Build AI-powered document ingestion and intelligent data extraction solutions with confidence scoring, traceability, auditability, and human-in-the-loop review capabilities.
• Develop embedded AI assistants using agentic workflows to improve productivity and automate business processes.
• Design and implement automated content generation, reporting, and presentation (deck) generation workflows powered by Large Language Models.
• Architect and implement Retrieval-Augmented Generation (RAG) solutions using advanced retrieval techniques, vector search, metadata filtering, and multi-stage retrieval pipelines.
• Develop and maintain scalable AI orchestration frameworks using LangChain or similar technologies for production-grade applications.
• Evaluate and recommend appropriate foundation models, orchestration strategies, prompt engineering techniques, and AI architecture best practices.
• Establish and maintain LLMOps processes including prompt lifecycle management, model versioning, evaluation frameworks, regression testing, observability, monitoring, and reliability measurement.
• Design AI-first data ingestion pipelines with measurable accuracy, quality, scalability, and operational reliability.
• Optimize retrieval quality using advanced techniques such as multi-vector search, late interaction (ColBERT), chunking strategies, metadata filtering, and re-ranking.
• Define and monitor evaluation metrics including Recall, Precision, MRR, NDCG, latency, and response quality to continuously improve AI system performance.
• Troubleshoot and resolve production AI issues including model regressions, prompt drift, retrieval degradation, hallucinations, and data quality challenges.
• Implement enterprise-grade governance controls including entitlements, access control, audit logging, and secure handling of Personally Identifiable Information (PII) within AI systems.
• Collaborate with cross-functional engineering, architecture, and business teams to integrate AI capabilities into enterprise applications.
• Act as a technical expert and contribute to defining enterprise-wide GenAI standards, reusable frameworks, and AI platform best practices.

Required Qualifications

• Bachelor's or master's degree in computer science, Artificial Intelligence, Software Engineering, Data Science, or a related technical discipline.
• 5+ years of software engineering experience with Python and/or Java.
• 2+ years of dedicated experience designing, developing, and operating production-grade Generative AI solutions in enterprise environments.
• Strong hands-on experience building GenAI orchestration frameworks beyond vendor examples (e.g., LangChain or similar frameworks).
• Proven expertise in developing production-grade AI applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), tool/function calling, agentic AI workflows, and structured output generation.
• Experience building AI-powered document ingestion, document intelligence, and automated data extraction solutions.
• Strong expertise in advanced retrieval technologies, including vector databases, semantic search, multi-vector retrieval, late-interaction approaches (e.g., ColBERT), intelligent chunking strategies, multi-stage retrieval pipelines, metadata filtering, and re-ranking techniques.
• Hands-on experience implementing AI-first data ingestion pipelines with measurable quality and reliability.
• Strong hands-on experience with LLMOps, including prompt engineering, prompt and version management, model evaluation frameworks, regression testing, monitoring, observability, and reliability measurement for production AI systems.
• Strong understanding of retrieval evaluation metrics such as Recall, Precision, Mean Reciprocal Rank (MRR), Normalized Discounted Cumulative Gain (NDCG), latency optimization, and answer quality.
• Experience diagnosing and mitigating production AI issues including model regressions, retrieval degradation, prompt drift, hallucinations, and data quality challenges.
• Experience implementing enterprise AI governance, security controls, auditability, and compliance requirements.
• Excellent analytical, communication, collaboration, and problem-solving skills.

Preferred Qualifications

• Experience in Fixed Income, Capital Markets, Institutional Lending, or Financial Services domains.
• Experience working in regulated enterprise environments with strong audit, compliance, and governance requirements.
• Familiarity with enterprise security frameworks, data governance policies, entitlement models, role-based access control (RBAC), and secure handling of Personally Identifiable Information (PII).
• Experience designing and developing reusable internal AI platforms, shared AI services, developer tooling, or enterprise AI frameworks.
• Front-end development experience using Angular or React for building AI-enabled user interfaces.
• Experience with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform (GCP).
• Knowledge of containerization and orchestration technologies such as Docker and Kubernetes.
• Experience developing RESTful APIs, microservices, and enterprise application integrations.
• Familiarity with CI/CD pipelines, DevOps, and MLOps best practices for AI deployment and lifecycle management.
• Experience contributing to enterprise AI architecture, platform ownership, and organization-wide GenAI standards.